KR102261049B1 - Method and system for detecting abnormal behavior for port security based on block chain - Google Patents

Abstract

The present disclosure relates to a block chain-based abnormal behavior detection method for port security. The block chain-based abnormal behavior detection method for port security includes the steps of: receiving semantic behavior information of an entity including a position and velocity of the entity within a port area; receiving a request to detect whether the entity behaves abnormally; when receiving the request, using a block chain network to detect whether the entity behaves abnormally based on the semantic behavior information of the entity; and transmitting, to a first user terminal, a detection result of whether the entity behaves abnormally.

Description

A block chain-based abnormal behavior detection method and system for port security {METHOD AND SYSTEM FOR DETECTING ABNORMAL BEHAVIOR FOR PORT SECURITY BASED ON BLOCK CHAIN}

The present disclosure relates to a block chain-based abnormal behavior detection method system for maintaining harbor security by detecting abnormal behavior of seafarers, etc. in a harbor area.

A port refers to offshore facilities for entry and exit of ships, embarkation and disembarkation of persons, and loading, unloading, storage and handling of cargo. In such a port, various facilities are required to promote safety following the loading and unloading of cargo and boarding and disembarking of crew members, or to maintain security in the harbor area. For example, for port security, RFID and NFC-based access management, CCTV-based video control, and manpower-centered monitoring and monitoring are currently being performed.

On the other hand, domestic officials can enter port facilities through access verification and pass issuance procedures, but no special access security measures are required for foreign seafarers working while anchored in the port. Therefore, security incidents such as smuggling attempts by foreign seafarers continue to occur. However, the existing CCTV monitoring is difficult to monitor even the shaded areas where the CCTV does not reach within the port facility, and it is difficult to predict the behavior of each foreign seafarer, making it difficult to prevent security accidents in advance.

The present disclosure provides a block chain-based abnormal behavior detection method for port security, a computer program stored in a recording medium, and an apparatus (system) for solving the above problems.

The present disclosure may be implemented in various ways including a method, an apparatus (system), or a computer program stored in a readable storage medium.

According to an embodiment of the present disclosure, a block chain-based abnormal behavior detection method for port security, performed by at least one processor, receives semantic behavior information of an entity including a position and speed of an entity in a port area step, receiving a request for detection of whether an object is abnormal, when receiving a request, using a block chain network to detect whether an object is acting abnormally based on semantic behavior information of the object, and and transmitting a detection result for abnormal behavior to the first user terminal.

According to an embodiment of the present disclosure, when receiving a request, the step of detecting whether an object is abnormal based on behavior information of the object using a block chain network includes using an agreed abnormal behavior detection algorithm, and detecting whether the object is behaving abnormally.

According to an embodiment of the present disclosure, the step of detecting whether an entity behaves abnormally by using an agreed abnormal behavior detection algorithm includes transmitting a validation request of the agreed abnormal behavior detection algorithm to a node on a blockchain network. and receiving a response to the validation request from a node on the blockchain network.

According to an embodiment of the present disclosure, the steps of receiving a first abnormal behavior detection algorithm from a second user terminal, comparing the performance of the first abnormal behavior detection algorithm with a second predetermined abnormal behavior detection algorithm, and comparing the performance The method further includes determining, based on the result, one of the first abnormal behavior detection algorithm and the second abnormal behavior detection algorithm as an agreed abnormal behavior detection algorithm.

According to an embodiment of the present disclosure, using the abnormal behavior detection smart contract, the method further includes transmitting an agreed abnormal behavior detection algorithm determined based on the performance comparison result to a plurality of nodes connected to the blockchain network. do.

According to an embodiment of the present disclosure, when the first abnormal behavior detection algorithm is determined as the agreed abnormal behavior detection algorithm, the method further includes transmitting a token reward to a second user terminal that has transmitted the first abnormal behavior detection algorithm. .

According to an embodiment of the present disclosure, the step of transmitting the token reward to the node connected to the block chain network that has transmitted the first abnormal behavior detection algorithm may include transferring the token reward determined based on the token reward smart contract to the second user terminal. It includes the step of transmitting.

According to an embodiment of the present disclosure, the token used for token reward includes a stable coin linked to real money.

According to an embodiment of the present disclosure, receiving a standard policy and token reward policy for improving the performance of an abnormal behavior detection algorithm, and rewarding a token according to the standard policy and token reward policy for improving the performance of an abnormal behavior detection algorithm The method further includes determining a smart contract.

According to an embodiment of the present disclosure, the method further includes transmitting the determined token reward smart contract to a plurality of nodes on the blockchain network.

According to an embodiment of the present disclosure, the receiving of the detection request for whether the object is abnormally behavior includes receiving a detection request for whether the object is abnormal when the location of the object is out of a predetermined area do.

According to an embodiment of the present disclosure, the receiving of the request for detection of whether the object is acting abnormally includes: receiving a request for detection of whether the object is acting abnormally when the amount of change in the speed of the object is greater than a predetermined criterion includes

According to an embodiment of the present disclosure, the step of receiving semantic behavior information of the entity including the position and velocity of the entity in the harbor area includes a data processing module for extracting the position and velocity of the entity by using the radio signal of the entity. and receiving semantic behavior information of the entity from

According to an embodiment of the present disclosure, the radio signal of the entity is received through a radar sensor installed in a port area.

According to an embodiment of the present disclosure, the semantic behavior information of the entity includes information on the type of the entity estimated from the position and velocity of the entity.

According to an embodiment of the present disclosure, the information about the type of entity includes information about a seafarer related to port security and information about a transport vehicle.

A computer program stored in a computer-readable recording medium is provided to execute the above-described block chain-based abnormal behavior detection method for port security according to an embodiment of the present disclosure in a computer.

An abnormal behavior detection system according to an embodiment of the present disclosure is connected to a communication module configured to receive semantic behavior information of an entity including a position and velocity of the entity in a port area, a memory and the memory, and a computer readable memory included in the memory at least one processor configured to execute at least one possible program. At least one processor receives a request to detect whether the object is abnormal, and upon receiving the request, detects whether the object is abnormal, based on semantic behavior information of the object, using a block chain network. and instructions for transmitting a detection result of whether or not an abnormal behavior of the user is performed to the first user terminal.

The at least one processor according to an embodiment of the present disclosure further includes an instruction for detecting whether an object is abnormal by using an agreed abnormal behavior detection algorithm.

At least one processor according to an embodiment of the present disclosure receives a first abnormal behavior detection algorithm from a node on a blockchain network, compares the performance of the first abnormal behavior detection algorithm and a second predetermined abnormal behavior detection algorithm, and , further comprising instructions for determining the first abnormal behavior detection algorithm or the second abnormal behavior detection algorithm as an agreed abnormal behavior detection algorithm based on a result of comparing the performance.

According to various embodiments of the present disclosure, it is possible to effectively detect an abnormal behavior of an entity made inside and outside a shadow area that is not included in the visual range of the CCTV, thereby enhancing stability, security, and the like in a port area.

In various embodiments of the present disclosure, by using a plurality of abnormal behavior detection systems connected to a block chain network to detect abnormal behavior of an entity in a port area, a smart contract is used while maintaining security for semantic behavior information of the entity, etc. Thus, the abnormal behavior detection algorithm can be efficiently managed.

In various embodiments of the present disclosure, by using a blockchain network and an abnormal behavior detection smart contract to efficiently process the distribution of an abnormal behavior detection algorithm and continuously update an algorithm with excellent performance, the performance of abnormal behavior detection continuously can improve

In various embodiments of the present disclosure, by using a blockchain network and a token reward smart contract, token rewards according to the distribution of abnormal behavior detection algorithms can be efficiently processed, and by providing token rewards, algorithms with excellent performance are continuously maintained. It can motivate developers to develop and deploy with

In various embodiments of the present disclosure, the user of the user terminal (eg, a security manager in the port area, etc.) receives the abnormal behavior information of the entity detected by the abnormal behavior detection system based on the semantic behavior information of the entity. It is possible to prevent or quickly recognize security incidents within.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Embodiments of the present disclosure will be described with reference to the accompanying drawings described below, wherein like reference numerals denote like elements, but are not limited thereto.
1 is a diagram illustrating an example of monitoring an entity in a port area and detecting an abnormal behavior of the entity according to an embodiment of the present disclosure;
2 is a schematic diagram illustrating a configuration in which an information processing system is connected to communicate with a plurality of user terminals in order to detect an abnormal behavior of an entity in a port area according to an embodiment of the present disclosure.
3 is a block diagram illustrating an internal configuration of a user terminal and an information processing system according to an embodiment of the present disclosure.
4 is a diagram illustrating an example of a plurality of abnormal behavior detection systems and block chain networks for detecting abnormal behavior of entities in a port area according to an embodiment of the present disclosure.
5 is a functional block diagram illustrating an internal configuration of an abnormal behavior detection system according to an embodiment of the present disclosure.
6 is a flowchart illustrating a process in which an abnormal behavior detection algorithm is agreed between a first user terminal, a second user terminal, a first abnormal behavior detection system, and a second abnormal behavior detection system according to an embodiment of the present disclosure.
7 is a flowchart illustrating a process in which a token reward is determined between a first user terminal, a second user terminal, a first abnormal behavior detection system, and a second abnormal behavior detection system according to an embodiment of the present disclosure.
8 is a flowchart illustrating a process in which abnormal behavior detection is performed between a data processing module, a first abnormal behavior detection system, a second abnormal behavior detection system, and a first user terminal according to an embodiment of the present disclosure.
9 is a flowchart illustrating an example of a block chain-based abnormal behavior detection method for port security according to an embodiment of the present disclosure.

Hereinafter, specific contents for carrying out the present disclosure will be described in detail with reference to the accompanying drawings. However, in the following description, if there is a risk of unnecessarily obscuring the subject matter of the present disclosure, detailed descriptions of well-known functions or configurations will be omitted.

In the accompanying drawings, identical or corresponding components are assigned the same reference numerals. In addition, in the description of the embodiments below, overlapping description of the same or corresponding components may be omitted. However, even if description regarding components is omitted, it is not intended that such components are not included in any embodiment.

Advantages and features of the disclosed embodiments, and methods of achieving them, will become apparent with reference to the embodiments described below in conjunction with the accompanying drawings. However, the present disclosure is not limited to the embodiments disclosed below, but may be implemented in various different forms, and only the present embodiments allow the present disclosure to be complete, and the present disclosure provides those skilled in the art with It is provided for complete information only.

Terms used in this specification will be briefly described, and the disclosed embodiments will be described in detail. The terms used in this specification have been selected as currently widely used general terms as possible while considering the functions in the present disclosure, but these may vary depending on the intention or precedent of a person skilled in the art, the emergence of new technology, and the like. In addition, in specific cases, there are also terms arbitrarily selected by the applicant, and in this case, the meaning will be described in detail in the description of the corresponding invention. Therefore, the terms used in the present disclosure should be defined based on the meaning of the term and the contents of the present disclosure, rather than the simple name of the term.

References in the singular herein include plural expressions unless the context clearly dictates that the singular is singular. Also, the plural expression includes the singular expression unless the context clearly dictates the plural. When it is said that a part includes a certain component throughout the specification, this means that other components may be further included, rather than excluding other components, unless otherwise stated.

In addition, the term 'module' or 'unit' used in the specification means a software or hardware component, and 'module' or 'unit' performs certain roles. However, 'module' or 'unit' is not meant to be limited to software or hardware. A 'module' or 'unit' may be configured to reside on an addressable storage medium or may be configured to refresh one or more processors. Thus, as an example, a 'module' or 'part' refers to components such as software components, object-oriented software components, class components, and task components, and processes, functions, and properties. , procedures, subroutines, segments of program code, drivers, firmware, microcode, circuitry, data, database, data structures, tables, arrays or at least one of variables. Components and 'modules' or 'units' are the functions provided therein that are combined into a smaller number of components and 'modules' or 'units' or additional components and 'modules' or 'units' can be further separated.

According to an embodiment of the present disclosure, a 'module' or a 'unit' may be implemented with a processor and a memory. 'Processor' should be construed broadly to include general purpose processors, central processing units (CPUs), microprocessors, digital signal processors (DSPs), controllers, microcontrollers, state machines, and the like. In some contexts, a 'processor' may refer to an application specific semiconductor (ASIC), a programmable logic device (PLD), a field programmable gate array (FPGA), or the like. 'Processor' refers to a combination of processing devices, such as, for example, a combination of a DSP and a microprocessor, a combination of a plurality of microprocessors, a combination of one or more microprocessors in combination with a DSP core, or any other such configurations. You may. Also, 'memory' should be construed broadly to include any electronic component capable of storing electronic information. 'Memory' means random access memory (RAM), read-only memory (ROM), non-volatile random access memory (NVRAM), programmable read-only memory (PROM), erase-programmable read-only memory (EPROM); may refer to various types of processor-readable media, such as electrically erasable PROM (EEPROM), flash memory, magnetic or optical data storage, registers, and the like. A memory is said to be in electronic communication with the processor if the processor is capable of reading information from and/or writing information to the memory. A memory integrated in the processor is in electronic communication with the processor.

In the present disclosure, 'blockchain' is a system in a distributed environment that can exchange generally digitized information, assets, or transactions, and uses a shared ledger (P2P) to peer) may refer to a system or platform that records the history of electronic transaction details that occur in the network. Here, the blockchain uses a decentralized or decentralized consensus mechanism, and a validating node on the network executes the same (or agreed upon) consensus algorithm on the same transaction history to determine the transaction history. can be approved (or disapproved). In addition, in the present disclosure, a 'node' refers to a computing device that can record, maintain, and store a shared ledger in a block chain, and has a computing power capable of recording and processing transaction details, such as block generation. can do.

In the present disclosure, a 'smart contract' may refer to a computer program configured to execute a specific contract recorded in the distributed ledger and record the execution result back to the distributed ledger. In this way, by using the smart contract to program the contract details recorded in the distributed ledger and register it in the block chain, it is possible to prevent forgery and falsification of the contract contents and to automatically execute the contract when the contract conditions are satisfied.

In the present disclosure, an 'entity' may include port officials passing through the port area, domestic and foreign sailors, or transport vehicles operating within the port area. Also, in the present disclosure, 'abnormal behavior' may refer to an entity's behavior that may threaten the security of a port, such as a smuggling attempt by a foreign seafarer or an intrusion of a security area.

In the present disclosure, 'semantic behavior information' refers to the semantic behavior of an entity in the harbor area, and may include information on the position, speed, position change, velocity change, type of entity, etc. of the entity. can Here, the information on the type of object includes information on seafarers related to port security, such as whether the object detected by the radio signal is a person or a vehicle, whether it is a foreign seafarer or a domestic seafarer, and information about a transport vehicle, etc. may include. That is, the information on the seafarer may include the nationality of the seafarer, entry time, etc., and the information on the transportation vehicle may include the number of the transportation vehicle, the time of entry and exit, information on the company related to the transportation vehicle, and the like.

In the present disclosure, a 'stable coin' may refer to a block-chain-based cryptocurrency issued to have a fixed value with respect to an existing currency, such as a dollar. In other words, a stable coin may refer to a virtual currency that has the same value or can be linked to the dollar, won, etc.

1 is a diagram illustrating an example of a configuration for monitoring an entity 120 in a harbor area 110 and detecting an abnormal behavior of the entity 120 according to an embodiment of the present disclosure. As shown, the port area 110 is a place where ships are anchored to transport domestic and foreign cargo, and may refer to an area through which an entity 120 such as a vehicle of a carrier or a crew member passes. According to an embodiment, in order to maintain the security of the port area 110 , continuous monitoring of vehicles, crews, etc. of a transport company passing through the port area 110 may be required.

According to an embodiment, the radar sensor 130 may monitor an entity within the port area 110 . The radar sensor 130 is a module capable of transmitting and receiving electromagnetic waves, and may transmit a radio signal and receive a radio signal that is reflected back by the object 120 in the port area 110 . Then, the radar sensor 130 may continuously monitor the position, motion, movement, etc. of the object 120 using the received radio signal. The radar sensor 130 may include a low frequency conversion board (eg, an RF board, etc.). By using the low-frequency conversion board, the radar sensor 130 may convert the analog signal reflected by the object 120 into a digital signal.

The radio signal of the object 120 (or the radio signal converted into a digital signal) received by the radar sensor 130 may be transmitted to a data processing module (not shown) for extracting the position and speed of the object. First, the data processing module may extract a change in the position of the object 120 by using the radio wave signal of the object 120 received by the radar sensor. Then, the data processing module may extract the velocity of the object 120 , the change in velocity, and the like by using the position change per time. That is, the data processing module may extract semantic behavior information of the entity 120 including the position and speed of the entity within the port area 110 . Here, the semantic behavior information may include arbitrary information indicating the behavior of the entity 120 in the harbor area 110 , for example, an entity estimated based on the position and speed of the entity 120 . 120) information about the type of information, etc., can be included. Here, the information on the type of the object 120 may include information on seafarers related to port security, information on transport vehicles, and the like. In other words, the semantic behavior information includes whether the entity 120 corresponds to a person (or whether it corresponds to a transport vehicle), whether the person is an administrator who entered the entry/exit place (or whether the person is a Korean or foreign sailor who has disembarked from a domestic or foreign vessel whether or not), and the like.

According to an embodiment, the semantic behavior information of the entity 120 extracted by the data processing module may be transmitted to a node on the block chain network for detecting whether the entity 120 is abnormal. A node on the block chain network that has received the semantic behavior information may detect whether the entity 120 is abnormally behavioral by using an abnormal behavior detection algorithm. For example, when a node on the block chain network receives a detection request for abnormal behavior of the entity 120 according to the semantic behavior information from the data processing module, it can detect whether the entity 120 is abnormal.

In FIG. 1 , it is illustrated that a person passing in the harbor area 110 is the entity 120 , but the present invention is not limited thereto, and the entity 120 is an arbitrary object (eg, transport vehicles, etc.). By the configuration of the above-described embodiments, it is possible to effectively detect an abnormal behavior of the entity 120 made inside and outside the shadow area of the CCTV, thereby enhancing stability and security in the port area 110 .

2 is a schematic diagram showing a configuration in which the information processing system 230 is connected to communicate with a plurality of user terminals 210_1, 210_2, and 210_3 in order to detect an abnormal behavior of an entity in a port area according to an embodiment of the present disclosure; to be. The information processing system 230 may refer to a system (eg, an abnormal behavior detection system) of nodes connected to a block chain network for abnormal behavior detection. The information processing system 230 communicates with the plurality of user terminals 210_1 , 210_2 , and 210_3 through the network 220 , and may exchange data necessary for detecting abnormal behavior. According to one embodiment, the information processing system 230 is one or more server devices capable of storing, providing, and executing computer-executable programs (eg, downloadable applications) and data related to the detection of abnormal behavior of objects in the port area. and/or a database, or one or more distributed computing devices and/or distributed databases based on cloud computing services. The detection result of whether the object is abnormal behavior performed by the information processing system 230 to the user (eg, an administrator who manages the security of the port area) through the plurality of user terminals 210_1, 210_2, 210_3 may be provided.

The plurality of user terminals 210_1 , 210_2 , and 210_3 may communicate with the information processing system 230 through the network 220 . Here, the user terminals 210_1 , 210_2 , and 210_3 may be terminals of an administrator who manages port area security, or terminals of a developer who develops and distributes a new abnormal behavior detection algorithm. The network 220 may be configured to enable communication between the plurality of user terminals 210_1 , 210_2 , and 210_3 and the information processing system 230 . Network 220 according to the installation environment, for example, Ethernet (Ethernet), wired home network (Power Line Communication), telephone line communication device and wired networks such as RS-serial communication, mobile communication network, WLAN (Wireless LAN), It may consist of wireless networks such as Wi-Fi, Bluetooth and ZigBee, or a combination thereof. The communication method is not limited, and the user terminals 210_1, 210_2, 210_3 as well as a communication method using a communication network (eg, a mobile communication network, a wired Internet, a wireless Internet, a broadcasting network, a satellite network, etc.) that the network 220 may include. ) may also include short-range wireless communication between

Although the mobile phone terminal 210_1, the tablet terminal 210_2, and the PC terminal 210_3 are illustrated as examples of the user terminal in FIG. 2, the present invention is not limited thereto, and the user terminals 210_1, 210_2, and 210_3 are wired and/or wireless communication. It can be any computing device capable of this. For example, a user terminal is a smartphone, a mobile phone, a navigation device, a computer, a notebook computer, a digital broadcasting terminal, a PDA (Personal Digital Assistants), a PMP (Portable Multimedia Player), a tablet PC, a game console (game console), a wearable device ( wearable devices), internet of things (IoT) devices, virtual reality (VR) devices, augmented reality (AR) devices, and the like. In addition, in FIG. 2 , three user terminals 210_1 , 210_2 , and 210_3 are illustrated as communicating with the information processing system 230 through the network 220 , but the present invention is not limited thereto, and a different number of user terminals is connected to the network ( It may be configured to communicate with information processing system 230 via 220 .

According to an embodiment, the information processing system 230 may receive, from the user terminals 210_1 , 210_2 , and 210_3 , a standard policy for performance improvement of an abnormal behavior detection algorithm, a token compensation policy, and the like. In this case, the information processing system 230 may agree on and distribute a smart contract for detecting abnormal behavior, a smart contract for token compensation, etc. based on the received policies. Thereafter, the information processing system 230 may transmit the distribution result of each smart contract to the user terminals 210_1 , 210_2 , and 210_3 .

According to an embodiment, the information processing system 230 may detect whether the object is abnormal based on an agreed abnormal behavior detection algorithm. That is, the information processing system 230 may detect whether the entity behaves abnormally based on the semantic behavior information of the entity. Then, the information processing system 230 may transmit the detection result of whether the abnormal behavior is present to the user terminals 210_1 , 210_2 , and 210_3 .

According to an embodiment, the information processing system 230 may receive a new abnormal behavior detection algorithm from the user terminals 210_1 , 210_2 , and 210_3 . When the received new abnormal behavior detection algorithm has better performance than the existing algorithm, the information processing system 230 may transmit the token reward calculated based on the token reward smart contract to the user terminals 210_1, 210_2, and 210_3. In this case, the token used for token reward may be a stable coin linked to real money.

3 is a block diagram illustrating the internal configuration of the user terminal 210 and the information processing system 230 according to an embodiment of the present disclosure. The user terminal 210 is a terminal of an administrator who manages security in a port area, or a terminal of a developer who develops and distributes an abnormal behavior detection algorithm, and may refer to any computing device capable of wired/wireless communication, for example, For example, the mobile phone terminal 210_1, the tablet terminal 210_2, the PC terminal 210_3 of FIG. 2 may be included. As shown, the user terminal 210 may include a memory 312 , a processor 314 , a communication module 316 , and an input/output interface 318 . Similarly, the information processing system 230 is a system (eg, an abnormal behavior detection system) of nodes connected on a blockchain network, including a memory 332 , a processor 334 , a communication module 336 and an input/output interface. (338). As shown in FIG. 3 , the user terminal 210 and the information processing system 230 are configured to communicate information and/or data via the network 220 using the respective communication modules 316 and 336 . can be In addition, the input/output device 320 may be configured to input information and/or data to the user terminal 210 through the input/output interface 318 or to output information and/or data generated from the user terminal 210 .

The memories 312 and 332 may include any non-transitory computer-readable recording medium. According to one embodiment, the memories 312 and 332 are non-volatile mass storage devices such as random access memory (RAM), read only memory (ROM), disk drives, solid state drives (SSDs), flash memory, and the like. (permanent mass storage device) may be included. As another example, a non-volatile mass storage device such as a ROM, an SSD, a flash memory, a disk drive, etc. may be included in the user terminal 210 or the information processing system 230 as a separate permanent storage device distinct from the memory. In addition, the memory 312 and 332 may store an operating system and at least one program code (eg, a code for detecting an abnormal behavior of an object in a port area installed and driven in the user terminal 210 ).

These software components may be loaded from a computer-readable recording medium separate from the memories 312 and 332 . Such a separate computer-readable recording medium may include a recording medium directly connectable to the user terminal 210 and the information processing system 230, for example, a floppy drive, a disk, a tape, a DVD/CD- It may include a computer-readable recording medium such as a ROM drive and a memory card. As another example, the software components may be loaded into the memories 312 and 332 through a communication module rather than a computer-readable recording medium. For example, the at least one program is a computer program (eg, associated with abnormal behavior detection in a port area) installed by files provided through the network 220 by developers or a file distribution system for distributing installation files of applications. application) may be loaded into the memories 312 and 332 .

The processors 314 and 334 may be configured to process instructions of a computer program by performing basic arithmetic, logic, and input/output operations. Instructions may be provided to the processor 314 , 334 by the memory 312 , 332 or the communication module 316 , 336 . For example, the processors 314 and 334 may be configured to execute received instructions according to program code stored in a recording device, such as the memories 312 and 332 .

The communication modules 316 and 336 may provide a configuration or function for the user terminal 210 and the information processing system 230 to communicate with each other via the network 220 , and the user terminal 210 and/or information processing The system 230 may provide a configuration or function for communicating with another user terminal or another system (eg, a separate cloud system, etc.). In one example, the processor 314 of the user terminal 210 generates a request or data (eg, a standard policy for improving the performance of an abnormal behavior detection algorithm) according to a program code stored in a recording device such as the memory 312 ; The token reward amount policy, the developed abnormal behavior detection algorithm, etc.) may be transmitted to the information processing system 230 through the network 220 under the control of the communication module 316 . Conversely, a control signal or command provided under the control of the processor 334 of the information processing system 230 is transmitted through the communication module 336 and the network 220 through the communication module 316 of the user terminal 210 . It may be received by the user terminal 210 . For example, the user terminal 210 may receive, from the information processing system 230 , through the communication module 316 , a detection result of whether an abnormal behavior exists, a token reward result according to distribution of an abnormal behavior detection algorithm, and the like.

The input/output interface 318 may be a means for interfacing with the input/output device 320 . As an example, an input device may include a device such as a camera, keyboard, microphone, mouse, etc. including an audio sensor and/or an image sensor, and an output device may include a device such as a display, speaker, haptic feedback device, etc. can As another example, the input/output interface 318 may be a means for an interface with a device in which a configuration or function for performing input and output, such as a touch screen, is integrated into one. For example, the processor 314 of the user terminal 210 uses information and/or data provided by the information processing system 230 or another user terminal in processing a command of a computer program loaded into the memory 312 . A service screen, etc. configured by doing this may be displayed on the display through the input/output interface 318 . In FIG. 3 , the input/output device 320 is illustrated not to be included in the user terminal 210 , but is not limited thereto, and may be configured as a single device with the user terminal 210 . In addition, the input/output interface 338 of the information processing system 230 is connected to the information processing system 230 or means for interfacing with a device (not shown) for input or output that the information processing system 230 may include. can be In FIG. 3, the input/output interfaces 318 and 338 are illustrated as elements configured separately from the processors 314 and 334, but the present invention is not limited thereto, and the input/output interfaces 318 and 338 may be configured to be included in the processors 314 and 334. have.

The user terminal 210 and the information processing system 230 may include more components than those of FIG. 3 . However, there is no need to clearly show most of the prior art components. According to an embodiment, the user terminal 210 may be implemented to include at least a portion of the above-described input/output device 320 . In addition, the user terminal 210 may further include other components such as a transceiver, a global positioning system (GPS) module, a camera, various sensors, and a database. For example, when the user terminal 210 is a smart phone, it may include components generally included in the smart phone, for example, an acceleration sensor, a gyro sensor, a camera module, various physical buttons, and touch. Various components such as a button using a panel, an input/output port, and a vibrator for vibration may be implemented to be further included in the user terminal 210 .

According to an embodiment, the processor 314 of the user terminal 210 may be configured to transmit a standard policy for improving the performance of an abnormal behavior detection algorithm and a token compensation amount policy. The processor 314 of the user terminal 210 receives information and/or data provided from the input/output device 320 through the input/output interface 318 or information and/or data from the information processing system 230 through the communication module 316 . Alternatively, data may be received, and the received information and/or data may be processed and stored in the memory 312 . In addition, such information and/or data may be provided to the information processing system 230 through the communication module 316 .

The processor 314 may receive text, an image, an image, etc. inputted or selected through an input device such as a camera, a microphone, etc. including a touch screen, a keyboard, an audio sensor and/or an image sensor connected to the input/output interface 318, The received text, image, and/or image may be stored in the memory 312 or provided to the information processing system 230 through the communication module 316 and the network 220 .

The processor 334 of the information processing system 230 may be configured to manage, process, and/or store information and/or data received from a plurality of user terminals and/or a plurality of external systems. According to an embodiment, the processor 334 may receive semantic behavior information of the entity including the position and speed of the entity in the port area, and receive a detection request for whether the entity is abnormal. Upon receiving the request, the processor 334 may detect whether the entity is acting abnormally based on semantic behavior information of the entity using the block chain network. Then, the processor 334 may transmit the detection result of whether the object is abnormally behaved to the user terminal 210 .

According to an embodiment, the processor 334 may receive a policy for improving the performance of the abnormal behavior detection algorithm and a token compensation policy from the user terminal 210 . Then, the processor 334 may determine the abnormal behavior detection smart contract, the token reward smart contract, and the like according to the standard policy for performance improvement of the abnormal behavior detection algorithm and the token reward policy. Here, the processor 334 may transmit the determined abnormal behavior detection smart contract, token reward smart contract, and the like to a plurality of nodes on the blockchain network.

According to an embodiment, the processor 334 may receive the first abnormal behavior detection algorithm from the user terminal 210 . In this case, the processor 334 compares the performance of the first abnormal behavior detection algorithm and the second predetermined abnormal behavior detection algorithm, and detects the first abnormal behavior detection algorithm and the second abnormal behavior detection algorithm based on the result of comparing the performance Any one of the algorithms may be determined as an agreed anomalous behavior detection algorithm. Then, the processor 334 may transmit the agreed abnormal behavior detection algorithm to a plurality of nodes connected on the block chain network by using the abnormal behavior detection smart contract.

According to an embodiment, when the newly developed and received first abnormal behavior detection algorithm is determined as the agreed abnormal behavior detection algorithm, the processor 334 rewards the user terminal 210 with the first abnormal behavior detection algorithm as a token reward. can be transmitted. In this case, the processor 334 may transmit the token reward determined based on the token reward smart contract to the user terminal 210 . Here, the token used for token compensation may correspond to a stable coin linked to real money.

4 is a diagram illustrating an example of a plurality of abnormal behavior detection systems 420 , 430 , 440 and a block chain network 450 for detecting abnormal behavior of an entity in a port area according to an embodiment of the present disclosure. As shown, a plurality of abnormal behavior detection systems 420 , 430 , 440 may be connected to the blockchain network 450 . In FIG. 4 , three abnormal behavior detection systems 420 , 430 , 440 are connected to and communicate with the blockchain network 450 , but the present invention is not limited thereto, and three or more abnormal behavior detection systems are connected to the blockchain network ( 450) can be connected.

Here, the plurality of abnormal behavior detection systems 420 , 430 , and 440 may include an abnormal behavior detection smart contract, a token reward smart contract, and the like. For example, the abnormal behavior detection smart contract is a plurality of abnormal behavior detection systems connected to the newly determined abnormal behavior detection algorithm on the block chain network 450 when the algorithm newly received from the user terminal is determined as the algorithm for abnormal behavior detection. It can be transmitted to (420, 430, 440). In addition, the token reward smart contract can determine the amount of token reward based on the superiority of the performance of the newly determined abnormal behavior detection algorithm, the degree of performance improvement compared to the previous algorithm, and the like.

According to an embodiment, the first abnormal behavior detection system 420 may receive semantic behavior information of an entity in the port area from the data processing module 410 . In other words, the first abnormal behavior detection system 420 may receive the semantic behavior information of the entity from the data processing module that extracts the position and velocity of the entity using the radio signal of the entity. Here, the semantic behavior information may include a change in position, speed, and speed of an entity, a type of an entity, nationality of the entity, and the like. Also, the first abnormal behavior detection system 420 may receive a detection request as to whether the object is abnormal behavior from the data processing module. For example, when the location of the object is out of a predetermined area, the first abnormal behavior detection system 420 may receive a request to detect whether the object is abnormal. In another example, when the amount of change in the speed of the object is greater than a predetermined criterion, the abnormal behavior detection system 420 may receive a request to detect whether the object is abnormal.

When receiving the request, the first abnormal behavior detection system 420 may detect whether the entity behaves abnormally based on semantic behavior information of the entity using the block chain network. For example, the abnormal behavior may correspond to a case in which an entity enters and exits an unauthorized area, a case in which the entity's speed rapidly increases toward a wall of a port area, and the like. The abnormal behavior of the entity may be detected by an abnormal behavior detection algorithm included in the first abnormal behavior detection system 420 . For example, the anomalous behavior detection algorithm is a predetermined algorithm to detect abnormal behavior by comprehensively considering changes in the position and speed of the object, the type of the object, the object's entry/exit route to the port area, and the time spent in the port area of the object. can

That is, the first abnormal behavior detection system 420 may detect whether the object is abnormal by using an agreed abnormal behavior detection algorithm. In this case, the validity of the abnormal behavior detection algorithm may be verified. Here, the validation may include a procedure of verifying whether the agreed anomalous behavior detection algorithm is the most up-to-date (agreed to have the best performance) algorithm. For example, the first abnormal behavior detection system 420 requests validation from the second abnormal behavior detection system 430, the third abnormal behavior detection system 440, etc. connected to the blockchain network 450, and the algorithm can be validated. In this case, the second abnormal behavior detection system 430 , the third abnormal behavior detection system 440 , etc. determine whether the algorithm received together with the validation request is the same as the algorithm it owns to determine the validity of the abnormal behavior detection algorithm. can be verified. As such, when the validity of the abnormal behavior detection algorithm is verified, the first abnormal behavior detection system 420 may detect whether the object is abnormal behavior using the corresponding algorithm.

According to an embodiment, the first abnormal behavior detection system 420 may transmit a detection result of whether the abnormal behavior is present to a user terminal (eg, a terminal of an administrator who manages security in a port area). With such a configuration, by using a plurality of abnormal behavior detection systems 420 , 430 , 440 connected on the block chain network 450 to detect an abnormal behavior of an entity in a port area, semantic behavior information of the entity, etc. It is possible to efficiently manage abnormal behavior detection algorithms by using smart contracts while maintaining security.

5 is a functional block diagram illustrating an internal configuration of an abnormal behavior detection system 520 according to an embodiment of the present disclosure. As shown, the abnormal behavior detection system 520 may include an abnormal behavior detection unit 512 , a token reward unit 514 , an algorithm verification unit 516 , and the like. The abnormal behavior detection system 520 communicates with the data processing module 510 , the user terminal 530 , and the like, and may send and receive data for detecting abnormal behavior of an entity in the port area.

According to an embodiment, the abnormal behavior detection unit 512 may receive semantic behavior information of the entity including the position and speed of the entity in the port area from the data processing module 510 . Also, the abnormal behavior detection unit 512 may receive a request for detection of whether the entity has abnormal behavior. When receiving the request, the abnormal behavior detection unit 512 may detect whether the entity is abnormal based on semantic behavior information of the entity using the block chain network. Here, the abnormal behavior detection algorithm is a predetermined and agreed algorithm, and may be an algorithm agreed to have the best performance among developed abnormal behavior detection algorithms. Then, the abnormal behavior detection unit 512 may transmit the detection result of whether the object is abnormal behavior to a user terminal (eg, a terminal of a security manager in a port area).

According to an embodiment, the token reward unit 514 may determine a token reward to be provided to a user who has distributed an abnormal behavior algorithm with improved performance. For example, a user who has developed a new first abnormal behavior detection algorithm may transmit the first abnormal behavior detection algorithm to the abnormal behavior detection system 520 using a user terminal. In other words, the token compensation unit 514 may receive the first abnormal behavior detection algorithm from the user terminal. In this case, the token compensation unit 514 may compare the performance of the first abnormal behavior detection algorithm and the predetermined second abnormal behavior detection algorithm. Here, the performance of the algorithm may be calculated based on the accuracy, efficiency, time complexity, and the like of abnormal behavior detection.

Then, the token compensation unit 514 may determine one of the first abnormal behavior detection algorithm and the second abnormal behavior detection algorithm as an agreed abnormal behavior detection algorithm based on the performance comparison result. Here, when it is determined that the performance of the newly developed first abnormal behavior detection algorithm is superior to that of the second abnormal behavior detection algorithm, the token compensation unit 514 sends the first abnormal behavior detection algorithm to the user terminal that has transmitted it. You can send token rewards. In this case, the amount of token reward transmitted to the user terminal may be determined based on the token reward smart contract. For example, the token reward amount may be calculated based on the degree of improvement in the performance of the algorithm, the period during which the new algorithm is developed, and the like.

According to an embodiment, the algorithm verification unit 516 may verify the validity of the agreed upon abnormal behavior detection algorithm. Specifically, the algorithm verification unit 516 transmits a request for validation of the agreed anomalous behavior detection algorithm to a node (eg, another abnormal behavior detection system) on the blockchain network, and performs validation from the node on the blockchain network. A response to the request may be received. Here, the validation of the abnormal behavior detection algorithm is to determine whether the abnormal behavior detection algorithm included in the abnormal behavior detection system 520 is the same as the algorithm determined as the best performing algorithm among a plurality of developed abnormal behavior detection algorithms. It may be a verification for That is, when the algorithm verification unit 516 transmits a validation request to another node, the other node determines whether the algorithm it owns and the corresponding algorithm are the same, and then returns a response to the validation request to the abnormal behavior detection system. It may transmit to 520 .

In FIG. 5 , the configuration of the abnormal behavior detection system 520 has been described separately for each function, but it does not necessarily mean that they are physically separated. For example, the abnormal behavior detection unit 512 and the algorithm verification unit 516 have been separately described above, but this is only to help the understanding of the present invention, and in one computing device installed in the abnormal behavior detection system 520 , two or more function can also be performed.

6 illustrates an abnormal behavior between a first user terminal 610 , a second user terminal 620 , a first abnormal behavior detection system 630 , and a second abnormal behavior detection system 640 according to an embodiment of the present disclosure. It is a flow chart showing the process in which the detection algorithm is agreed. According to an embodiment, the first user terminal 610 may be a terminal of an administrator managing security in a port area, and the second user terminal 620 may be a terminal of a developer who develops an abnormal behavior detection algorithm. In addition, the first abnormal behavior detection system 630 and the second abnormal behavior detection system 640 may be systems including nodes constituting a block chain network for abnormal behavior detection. The first user terminal 610 and the second user terminal 620 communicate with the first abnormal behavior detection system 630 or the second abnormal behavior detection system 640 connected to the block chain network, and Data can be exchanged for detecting abnormal behavior.

According to an embodiment, the first user terminal 610 may transmit the policy 612 for performance improvement of the abnormal behavior detection algorithm to the first abnormal behavior detection system 630 . Here, the policy 612 for improving the performance of the abnormal behavior detection algorithm is changed or updated to an algorithm having a newer or higher performance compared to the previously used predetermined abnormal behavior detection algorithm, the abnormal behavior detection smart contract It may be a policy for configuring The first abnormal behavior detection system 630 that has received the policy 612 may distribute the abnormal behavior detection smart contract 632 according to the policy to the second abnormal behavior detection system 640 connected to the block chain network. .

Then, the first abnormal behavior detection system 630 may receive a distribution request 622 of the abnormal behavior detection algorithm from the second user terminal 620 . Upon receiving the distribution request 622 , the first abnormal behavior detection system 630 may compare performance of a plurality of abnormal behavior detection algorithms ( 634 ). Specifically, the first abnormal behavior detection system 630 may compare the performance of a previously used predetermined abnormal behavior detection algorithm and a newly received abnormal behavior detection algorithm. Here, the performance of the algorithm may be calculated based on the accuracy, efficiency, and time complexity of detecting abnormal behavior of each algorithm.

Based on the result of comparing the performance of the algorithms, the first abnormal behavior detection system 630 may determine any one of the newly received abnormal behavior detection algorithm and the predetermined abnormal behavior detection algorithm as the agreed abnormal behavior detection algorithm. That is, the first abnormal behavior detection system 630 may select one abnormal behavior detection algorithm with excellent performance. Here, when the new abnormal behavior detection algorithm is determined as the new algorithm, the corresponding algorithm may be determined as the new version algorithm. In this case, the first abnormal behavior detection system 630 may distribute the selected abnormal behavior detection algorithm to the second abnormal behavior detection system 640 connected to the block chain network by using the abnormal behavior detection smart contract (636). ). As a result, all systems connected to the blockchain network can include the same anomaly detection algorithm. Also, the first abnormal behavior detection system 630 may transmit a response 638 to the distribution result to the second user terminal 620 .

In FIG. 6 , it is illustrated that one terminal of the manager and one terminal of the developer exist, but the present invention is not limited thereto, and two or more manager's terminals or two or more developer's terminals may exist. In this case, each user terminal communicates with any one system or two or more systems connected to the block chain network, and can send and receive data necessary for detecting abnormal behavior. With this configuration, by using the block chain network and abnormal behavior detection smart contract, the distribution of abnormal behavior detection algorithm is efficiently processed, and the algorithm with excellent performance is continuously updated, thereby continuously improving the performance of abnormal behavior detection. can do it

7 is a token reward between a first user terminal 610 , a second user terminal 620 , a first abnormal behavior detection system 630 , and a second abnormal behavior detection system 640 according to an embodiment of the present disclosure. This is a flow chart showing the determination process. As described above, the first user terminal 610 may be a terminal of an administrator who manages security in the port area, and the second user terminal 620 may be a terminal of a developer who develops an abnormal behavior detection algorithm. In addition, the first abnormal behavior detection system 630 and the second abnormal behavior detection system 640 may be systems of nodes constituting a block chain network for abnormal behavior detection. The first user terminal 610 and the second user terminal 620 communicate with the first abnormal behavior detection system 630 or the second abnormal behavior detection system 640 connected to the block chain network, and Data can be exchanged for detecting abnormal behavior.

According to an embodiment, the first user terminal 610 may transmit the token reward amount policy 712 to the first abnormal behavior detection system 630 . Here, the token reward amount policy 712 may be a policy for configuring the token reward smart contract to determine the token reward amount to be provided to the user who has distributed the abnormal behavior detection algorithm. The first abnormal behavior detection system 630 receiving the policy 712 may distribute the token reward smart contract 732 according to the policy to the second abnormal behavior detection system 640 connected to the block chain network.

Then, the first abnormal behavior detection system 630 may receive a request 722 to distribute the abnormal behavior detection algorithm from the second user terminal 620 . As described above in FIG. 6 , upon receiving the distribution request 722 , the first abnormal behavior detection system 630 may perform performance comparison of algorithms. Then, when the algorithm received from the second user terminal 620 is agreed upon as the new abnormal behavior detection algorithm, the first abnormal behavior detection system 630 may determine a token reward to be provided to the second user terminal 620 . There is (734). In this case, token compensation may be performed using a token compensation smart contract created based on the token compensation amount policy. For example, the token reward may be determined by comprehensively considering the accuracy of the algorithm, the degree to which the performance of the algorithm is improved, and the period until a new algorithm is agreed upon.

When the token reward is determined, the first abnormal behavior detection system 630 may transmit a response 736 to the reward result to the second user terminal 620 to which the algorithm is distributed. In this case, the first abnormal behavior detection system 630 may transmit the token reward determined based on the token reward smart contract together with the response 736 to the reward result to the second user terminal 620 .

Although it is illustrated in FIG. 7 that one terminal of the manager and one terminal of the developer exist, the present invention is not limited thereto, and two or more terminals of the manager or two or more terminals of the developer may exist. In this case, each user terminal communicates with any one system or two or more systems connected to the block chain network, and can send and receive data necessary for detecting abnormal behavior. With such a configuration, by using the blockchain network and token reward smart contract, token rewards according to the distribution of abnormal behavior detection algorithms can be efficiently processed, and algorithms with excellent performance can be continuously developed by providing token rewards. and can motivate developers to deploy.

8 is a diagram illustrating abnormal behavior detection between the data processing module 810 , the first abnormal behavior detection system 820 , the second abnormal behavior detection system 830 , and the first user terminal 840 according to an embodiment of the present disclosure. It is a flow chart showing the process in which According to an embodiment, the data processing module 810 may be a module for extracting semantic behavior information including the location and speed of the object based on the radio signal of the object in the port area. In addition, the first user terminal 840 may be a terminal of an administrator who manages security in the port area, and the first abnormal behavior detection system 820 and the second abnormal behavior detection system 830 are blocks for abnormal behavior detection. It may be a system of nodes constituting a chain network.

According to an embodiment, the data processing module 810 may transmit an abnormal behavior detection request 812 to the first abnormal behavior detection system 820 . In this case, the data processing module 810 may transmit semantic behavior information of the entity including the position and speed of the entity in the port area together with the abnormal behavior detection request 812 . For example, the data processing module 810 requests abnormal behavior detection to the first abnormal behavior detection system 820 when the location of the object is out of a predetermined area or the change amount of the object's speed is greater than a predetermined reference. 812) can be transmitted.

Upon receiving the request 812 , the first abnormal behavior detection system 820 may transmit an algorithm validation request 822 to the second abnormal behavior detection system 830 connected to the blockchain network. Then, the first abnormal behavior detection system 820 may receive a response 832 to the validation request from the second abnormal behavior detection system 830 . That is, since the version of the abnormal behavior detection algorithm stored in the first abnormal behavior detection system 820 is continuously updated, validation may be performed to check whether the algorithm currently stored in the system is the latest algorithm.

When the validity of the abnormal behavior detection algorithm is verified, the first abnormal behavior detection system 820 may perform abnormal behavior detection of an object in the port area using the verified algorithm ( 824 ). For example, the first abnormal behavior detection system 820 may detect an abnormal behavior of the entity, such as when the entity enters a security area, moves to a location not related to a task, or increases in speed rapidly. Then, the first abnormal behavior detection system 820 may transmit the abnormal behavior detection result 826 to the first user terminal 840 . A user of the first user terminal (eg, a security manager in the port area, etc.) may prevent or quickly recognize a security incident in the port area based on the received content.

9 is a flowchart illustrating an example of a block chain-based abnormal behavior detection method 900 for port security according to an embodiment of the present disclosure. According to an embodiment, the block chain-based abnormal behavior detection method 900 may be performed by an abnormal behavior detection system (eg, at least one processor of the abnormal behavior detection system). The abnormal behavior detection method 900 may be started when the processor receives semantic behavior information of the entity including the position and speed of the entity in the harbor area (S910). For example, the processor may receive the semantic behavior information of the object from the data processing module that extracts the position and velocity of the object by using the propagation signal of the object. In this case, the radio signal of the entity may be received through a radar sensor installed in the port area.

Then, the processor may also receive a request for detection of whether the object is abnormal (S920). For example, when the location of the object is out of a predetermined area, the processor may receive a request to detect whether the object is acting abnormally. In another example, when the amount of change in the speed of the object is greater than a predetermined criterion, the processor may receive a request to detect whether the object is acting abnormally.

When receiving the request, the processor may detect whether the entity behaves abnormally on the basis of the semantic behavior information of the entity using the block chain network (S930). For example, the processor may detect whether the object is abnormal by using an agreed abnormal behavior detection algorithm. In this case, the processor may transmit a request for validation of the agreed anomalous behavior detection algorithm to a node on the blockchain network, and receive a response to the validation request from the node on the blockchain network.

The processor may transmit a detection result of whether or not the entity behaves abnormally to the first user terminal (S940). In this case, the first user terminal may be a terminal of an administrator who manages security in the port area. As described above, the first user terminal receiving the detection result of whether the abnormal behavior is abnormal may output an alarm when the abnormal behavior is detected.

The above-described block chain-based abnormal behavior detection method for port security may be provided as a computer program stored in a computer-readable recording medium for execution in a computer. The medium may be to continuously store a computer executable program, or to temporarily store it for execution or download. In addition, the medium may be various recording means or storage means in the form of a single or several hardware combined, it is not limited to a medium directly connected to any computer system, and may exist distributed on a network. Examples of the medium include a hard disk, a magnetic medium such as a floppy disk and a magnetic tape, an optical recording medium such as CD-ROM and DVD, a magneto-optical medium such as a floppy disk, and those configured to store program instructions, including ROM, RAM, flash memory, and the like. In addition, examples of other media may include recording media or storage media managed by an app store that distributes applications, sites that supply or distribute various other software, and servers.

The method, operation, or techniques of this disclosure may be implemented by various means. For example, these techniques may be implemented in hardware, firmware, software, or a combination thereof. Those of ordinary skill in the art will appreciate that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the disclosure herein may be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate this interchange of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design requirements imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementations should not be interpreted as causing a departure from the scope of the present disclosure.

In a hardware implementation, the processing units used to perform the techniques include one or more ASICs, DSPs, digital signal processing devices (DSPDs), programmable logic devices (PLDs). ), field programmable gate arrays (FPGAs), processors, controllers, microcontrollers, microprocessors, electronic devices, other electronic units designed to perform the functions described in this disclosure. , a computer, or a combination thereof.

Accordingly, the various illustrative logic blocks, modules, and circuits described in connection with this disclosure are suitable for use in general purpose processors, DSPs, ASICs, FPGAs or other programmable logic devices, discrete gate or transistor logic, discrete hardware components, or the present disclosure. It may be implemented or performed in any combination of those designed to perform the functions described in A general purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, eg, a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other configuration.

In firmware and/or software implementations, the techniques include random access memory (RAM), read-only memory (ROM), non-volatile random access memory (NVRAM), PROM ( on computer readable media such as programmable read-only memory), erasable programmable read-only memory (EPROM), electrically erasable PROM (EEPROM), flash memory, compact disc (CD), magnetic or optical data storage devices, etc. It may be implemented as stored instructions. The instructions may be executable by one or more processors and may cause the processor(s) to perform certain aspects of the functionality described in this disclosure.

Although the embodiments described above have been described utilizing aspects of the presently disclosed subject matter in one or more standalone computer systems, the present disclosure is not so limited and may be implemented in connection with any computing environment, such as a network or distributed computing environment. . Still further, aspects of the subject matter in this disclosure may be implemented in a plurality of processing chips or devices, and storage may be similarly affected across the plurality of devices. Such devices may include PCs, network servers, and portable devices.

Although the present disclosure has been described in connection with some embodiments herein, various modifications and changes may be made without departing from the scope of the present disclosure that can be understood by those skilled in the art to which the present disclosure pertains. Further, such modifications and variations are intended to fall within the scope of the claims appended hereto.

110: harbor area 120: entity
130: radar sensor

Claims (20)

In the block chain-based abnormal behavior detection method for port security, performed by at least one processor,
receiving semantic behavior information of the entity including the position and velocity of the entity within the harbor area;
receiving a detection request as to whether the entity behaves abnormally;
when receiving the request, detecting whether the entity behaves abnormally based on semantic behavior information of the entity using a block chain network; and
Transmitting the detection result of whether the object is abnormal behavior to a first user terminal
includes,
When receiving the request, the step of detecting whether the entity is abnormal based on the semantic behavior information of the entity using a block chain network includes:
transmitting a request for validation of an agreed abnormal behavior detection algorithm to a node on the blockchain network;
receiving a response to a validation request from a node on the blockchain network;
receiving a first abnormal behavior detection algorithm from a second user terminal;
comparing the performance of the first abnormal behavior detection algorithm and a second predetermined abnormal behavior detection algorithm;
determining one of the first abnormal behavior detection algorithm and the second abnormal behavior detection algorithm as the agreed abnormal behavior detection algorithm based on a result of comparing the performance; and
Using an abnormal behavior detection smart contract, transmitting the agreed abnormal behavior detection algorithm determined based on a result of comparing the performance to a plurality of nodes connected to the block chain network
Including, a block chain-based abnormal behavior detection method for port security.

delete delete delete delete According to claim 1,
When the first abnormal behavior detection algorithm is determined as the agreed abnormal behavior detection algorithm, transmitting a token reward to the second user terminal that has transmitted the first abnormal behavior detection algorithm;
A block chain-based abnormal behavior detection method for port security, further comprising a.
7. The method of claim 6,
The step of transmitting a token reward to a node connected to the blockchain network that has transmitted the first abnormal behavior detection algorithm comprises:
Transmitting the token reward determined based on the token reward smart contract to the second user terminal
Including, a block chain-based abnormal behavior detection method for port security.
7. The method of claim 6,
The token used for the token reward includes a stable coin linked to real money, a block chain-based abnormal behavior detection method for port security.
8. The method of claim 7,
receiving a standard policy for improving the performance of the abnormal behavior detection algorithm and a token reward policy; and
determining the token reward smart contract according to the standard policy for performance improvement of the abnormal behavior detection algorithm and the token reward amount policy
A block chain-based abnormal behavior detection method for port security, further comprising a.
10. The method of claim 9,
transmitting the determined token reward smart contract to a plurality of nodes on the blockchain network;
A block chain-based abnormal behavior detection method for port security, further comprising a.
According to claim 1,
Receiving a request for detection of whether the entity is abnormally behaves,
Receiving a detection request as to whether the object has abnormal behavior when the location of the object is out of a predetermined area
Including, a block chain-based abnormal behavior detection method for port security.
According to claim 1,
Receiving a request for detection of whether the entity is abnormally behaves,
Receiving a detection request as to whether the entity behaves abnormally when the amount of change in the velocity of the entity is greater than a predetermined criterion;
Including, a block chain-based abnormal behavior detection method for port security.
According to claim 1,
Receiving the semantic behavior information of the entity including the position and velocity of the entity in the harbor area comprises:
Receiving semantic behavior information of the entity from a data processing module that extracts the position and velocity of the entity by using a radio signal reflected by the entity
Including, a block chain-based abnormal behavior detection method for port security.
14. The method of claim 13,
The radio signal reflected by the entity is received through a radar sensor installed in the port area, a block chain-based abnormal behavior detection method for port security.
According to claim 1,
The semantic behavior information of the entity includes information on the type of the entity estimated from the position and speed of the entity, a block chain-based abnormal behavior detection method for port security.
16. The method of claim 15,
The information on the type of entity, including information on the crew and transport vehicles related to port security, a block chain-based abnormal behavior detection method for port security.
A computer program stored in a computer-readable recording medium to execute the block chain-based abnormal behavior detection method for port security according to any one of claims 1 and 6 to 16 in a computer.
A system for detecting anomalies, comprising:
a communication module configured to receive semantic behavior information of the entity including the position and speed of the entity in the port area, and receive a detection request as to whether the entity is abnormal;
Memory; and
at least one processor coupled to the memory and configured to execute at least one computer readable program contained in the memory;
the at least one processor,
When receiving the request, using a block chain network to detect abnormal behavior of the entity based on semantic behavior information of the entity,
executing commands for transmitting a detection result of whether the entity is abnormally behaved to a first user terminal,
When receiving the request, detecting whether the entity behaves abnormally on the basis of the semantic behavior information of the entity using a block chain network,
sending a request for validation of the agreed anomalous behavior detection algorithm to a node on the blockchain network;
receiving a response to a validation request from a node on the blockchain network,
receiving the first abnormal behavior detection algorithm from the second user terminal,
comparing the performance of the first abnormal behavior detection algorithm and a second predetermined abnormal behavior detection algorithm;
determining one of the first abnormal behavior detection algorithm and the second abnormal behavior detection algorithm as the agreed abnormal behavior detection algorithm based on a result of comparing the performance;
Using an abnormal behavior detection smart contract, comprising transmitting the agreed abnormal behavior detection algorithm determined based on a result of comparing the performance to a plurality of nodes connected on the blockchain network,
Abnormal behavior detection system.
19. The method of claim 18,
the at least one processor,
The abnormal behavior detection system further executes a command for detecting whether the object is abnormal by using the agreed abnormal behavior detection algorithm.
delete

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