AU2021104400A4 - An intelligent system for detecting behavioral cyber attack on industrial iot using ai federated learning algorithm - Google Patents
An intelligent system for detecting behavioral cyber attack on industrial iot using ai federated learning algorithm Download PDFInfo
- Publication number
- AU2021104400A4 AU2021104400A4 AU2021104400A AU2021104400A AU2021104400A4 AU 2021104400 A4 AU2021104400 A4 AU 2021104400A4 AU 2021104400 A AU2021104400 A AU 2021104400A AU 2021104400 A AU2021104400 A AU 2021104400A AU 2021104400 A4 AU2021104400 A4 AU 2021104400A4
- Authority
- AU
- Australia
- Prior art keywords
- cloud server
- cyberattack
- detecting
- machine learning
- data
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Ceased
Links
- 230000003542 behavioural effect Effects 0.000 title abstract description 5
- 238000010801 machine learning Methods 0.000 claims abstract description 29
- 238000000034 method Methods 0.000 claims abstract description 28
- 238000004519 manufacturing process Methods 0.000 claims description 16
- 238000004458 analytical method Methods 0.000 claims description 10
- 238000012545 processing Methods 0.000 claims description 4
- 238000010586 diagram Methods 0.000 claims description 3
- 230000008569 process Effects 0.000 description 6
- 230000008901 benefit Effects 0.000 description 4
- 238000012986 modification Methods 0.000 description 4
- 230000004048 modification Effects 0.000 description 4
- 238000012549 training Methods 0.000 description 3
- 230000005540 biological transmission Effects 0.000 description 2
- 230000006870 function Effects 0.000 description 2
- 230000002159 abnormal effect Effects 0.000 description 1
- 230000003466 anti-cipated effect Effects 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 230000007123 defense Effects 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000002708 enhancing effect Effects 0.000 description 1
- 230000010365 information processing Effects 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L63/00—Network architectures or network communication protocols for network security
- H04L63/14—Network architectures or network communication protocols for network security for detecting or protecting against malicious traffic
- H04L63/1408—Network architectures or network communication protocols for network security for detecting or protecting against malicious traffic by monitoring network traffic
- H04L63/1416—Event detection, e.g. attack signature detection
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16Y—INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE INTERNET OF THINGS [IoT]
- G16Y30/00—IoT infrastructure
- G16Y30/10—Security thereof
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04Q—SELECTING
- H04Q2209/00—Arrangements in telecontrol or telemetry systems
- H04Q2209/20—Arrangements in telecontrol or telemetry systems using a distributed architecture
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04Q—SELECTING
- H04Q2209/00—Arrangements in telecontrol or telemetry systems
- H04Q2209/40—Arrangements in telecontrol or telemetry systems using a wireless architecture
Landscapes
- Engineering & Computer Science (AREA)
- Computer Security & Cryptography (AREA)
- Computing Systems (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Computer Hardware Design (AREA)
- General Engineering & Computer Science (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Medical Informatics (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
AN INTELLIGENT SYSTEM FOR DETECTING BEHAVIORAL CYBER
ATTACK ON INDUSTRIAL IoT USING Al FEDERATED LEARNING
5 ALGORITHM
Aspects of the present disclosure relate to an intelligent system (100) used for detecting
behavioral cyberattack on industrial IoT using Al federated learning algorithm. The system
(100) comprises of a plurality of device (102) for managing the various operations in the
industry, a plurality of cloud server (104) for each of the industry, another cloud server
10 (106) which connects all the individual cloud server and a user interface (108) for
receiving alert during the cyberattack. The invention further discloses a method (200) for
detecting cyberattack using the federated learning. The method (200) comprises steps of
deploying (202) the machine learning model in the plurality of cloud server (104). Then
collecting (204) the data related to various industrial operation. Then sending (206) the
15 collected data wirelessly to the cloud servers. Then the data is analyzed for vulnerable
endpoints using the machine learning model on the plurality of cloud server (104). Finally,
the user interface (108) is alerted and the data is shared with plurality of cloud server (104)
by using another cloud server (106).
20 (FIG. 1 will be the reference figure)
25
- 13 -
rage L 01 L
100r
Deploying a machine leading model for identifying any
1cyberattack 102
Collecting the data related to the industries
104
Sending the data collected to the plurality of cloud
servers tirelessly 106
Analyzing the data sent by the plurality of devices for
any possible cyberattack 108
Alerting by sending the alert to the user interface
FIG._ 2 lwcatofmtodfrdeetngcuig eeatdmchn ann110
FIG. 2Flowchart of method for detecting cyberattack using federated machine learning
2
Description
rage L 01 L
100r Deploying a machine leading model for identifying any 1cyberattack 102
Collecting the data related to the industries 104
Sending the data collected to the plurality of cloud servers tirelessly 106
Analyzing the data sent by the plurality of devices for any possible cyberattack 108
Alerting by sending the alert to the user interface FIG._ 2 lwcatofmtodfrdeetngcuig eeatdmchn ann110
FIG. 2Flowchart of method for detecting cyberattack using federated machine learning
AN INTELLIGENT SYSTEM FOR DETECTING BEHAVIORAL CYBER ATTACK ON INDUSTRIAL IoT USING Al FEDERATED LEARNING ALGORITHM
[00011 The present disclosure relates to a protection of IloT from cyberattack and in particular to an intelligent system for detecting behavioral cyberattack on industrial IoT using Al federated learning algorithm.
[0002] Background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
[0003] The industrial IoT or IloT is the way of using the smart sensors and actuators for enhancing the manufacturing process and other industrial processes. IloT is also known as the industrial internet or industry 4.0 that has the capability of using the smart machines as well as the real-time analytics which enables the user to take the advantage of these dumb machines which were produced years before. The use of connected sensors and actuators enables the companies to see their inefficiencies and solve the problem sooner. It also enables the user in maintaining the quality, sustainability as well as promote green practices in the industry.
[0004] Federated machine learning is the next generation method of training machines as well as model. In the standard machine learning approaches, it is required that the data be present at some central location or in one machine for training. The federated learning enables all the system to collaboratively learn a shared prediction model while keeping all the data on the device. This all is possible due to the capability of the secure and robust cloud infrastructure.
[0005] The problem with the distributed system for identification for cyberattack is that once a certain cyberattack happens on a certain system then only that system's cyberattack detection model is able to learn from that attack. This update is not shared with every system. To solve this problem, it is best to go from a federated learning model which enables every system connected to a network to train the model with the latest cyberattack which has happened on any system in the network.
[00061 In the related prior art, efforts have been made to provide different solutions for protecting industrial system from cyberattack. For example, United States patent no. US10176320B1 discloses a security system and method for using machine learning to improve cybersecurity operations in an industrial control networks and other systems. A method includes collecting, by a security system, current process information for a plurality of processes in a control system. The method includes analyzing, by the security system, the current process information according to one or more process models. The method includes producing, by the security system and according to the analysis, a risk report that identifies an abnormal process among the plurality of processes. However, the present system doesn't uses the federated machine learning for protecting the systems against the cyberattack.
[00071 In some embodiments, the numbers expressing quantities or dimensions of items, and so forth, used to describe and claim certain embodiments of the invention are to be understood as being modified in some instances by the term "about." Accordingly, in some embodiments, the numerical parameters set forth in the written description and attached claims are approximations that can vary depending upon the desired properties sought to be obtained by a particular embodiment. In some embodiments, the numerical parameters should be construed in light of the number of reported significant digits and by applying ordinary rounding techniques. Notwithstanding that the numerical ranges and parameters setting forth the broad scope of some embodiments of the invention are approximations, the numerical values set forth in the specific examples are reported as precisely as practicable. The numerical values presented in some embodiments of the invention may contain certain errors necessarily resulting from the standard deviation found in their respective testing measurements.
[0008] The recitation of ranges of values herein is merely intended to serve as a shorthand method of referring individually to each separate value falling within the range. Unless otherwise indicated herein, each individual value is incorporated into the specification as if it were individually recited herein. All methods described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The use of any and all examples, or exemplary language (e.g. "such as") provided with respect to certain embodiments herein is intended merely to better illuminate the invention and does not pose a limitation on the scope of the invention otherwise claimed. No language in the specification should be construed as indicating any non-claimed element essential to the practice of the invention.
[00091 It is an object of the present disclosure to provide a system for protecting against the cyberattack on industry.
[0010] The present concept of the present invention is directed towards thea system for detecting cyberattack using federated machine learning, wherein said system comprises of: a plurality of devices for managing the production, supply, manufacturing and analysis, installed at various industries; a plurality of cloud server, connected to the plurality of devices, for processing, analyzing and storing a data sent or received from the plurality of devices; an another cloud server, connected to the plurality of cloud server; a user interface, connected to the cloud server, for alerting during a cyberattack.
[0011] In an aspect, the plurality of devices are smart grids, autonomous transportation devices, gas pipeline devices or any other cyber physical devices. The plurality of devices in the industries are connected with each other through Wi-Fi, Bluetooth or Zigbee for receiving or transmitting the data to-fro from the cloud server. The user interface includes a personal computer, personal digital assistant, tablet device, or smart phone, connected to the cloud server wirelessly.
[0012] In an aspect, a method for detecting cyberattack using federated machine learning, wherein said method comprises steps of: deploying, by the plurality of cloud server, a machine learning model for identifying any cyberattack; collecting, by the plurality of devices, the data related to the production, supply, manufacturing and analysis in the industries; sending, by the plurality of the devices, the data collected to the plurality of cloud servers wirelessly; analyzing, by the plurality of cloud servers, the data sent by the plurality of devices for any possible cyberattack; alerting, by the another cloud server, by sending the alert to the user interface after analyzing the data sent by the plurality of cloud servers.
[0013] In an aspect, the cloud server sends the alert to the user interface in a form of a risk report. The data is analysed by analyzing the vulnerable endpoints.
[0014] One should appreciate that although the present disclosure has been explained with respect to a defined set of functional modules, any other module or set of modules can be added/deleted/modified/combined, and any such changes in architecture/construction of the proposed system are completely within the scope of the present disclosure. Each module can also be fragmented into one or more functional sub-modules, all of which also completely within the scope of the present disclosure.
[0015] Various objects, features, aspects and advantages of the inventive subject matter will become more apparent from the following detailed description of preferred embodiments, along with the accompanying drawing figures in which like numerals represent like components.
[00161 The accompanying drawings are included to provide a further understanding of the present disclosure and are incorporated in and constitute a part of this specification. The drawings illustrate exemplary embodiments of the present disclosure and, together with the description, serve to explain the principles of the present disclosure.
[00171 FIG. 1 illustrates an exemplaryblock diagram of the system for detecting cyberattack using federated machine learning.
[0018] FIG. 2 illustrates an exemplary flowchart of method for detecting cyberattack using federated machine learning.
[0019] The accompanying drawings are included to provide a further understanding of the present disclosure, and are incorporated in and constitute a part of this specification. The drawings illustrate exemplary embodiments of the present disclosure and, together with the description, serve to explain the principles of the present disclosure.
[0020] Other objects, advantages and novel features of the invention will become apparent from the following detailed description of the present embodiment when taken in conjunction with the accompanying drawings.
[0021] In the following description, numerous specific details are set forth in order to provide a thorough understanding of embodiments of the present invention. It will be apparent to one skilled in the art that embodiments of the present invention may be practiced without some of these specific details.
[0022] Embodiments of the present invention include various steps, which will be described below. The steps may be performed by hardware components or may be embodied in machine-executable instructions, which may be used to cause a general purpose or special-purpose processor programmed with the instructions to perform the steps. Alternatively, steps may be performed by a combination of hardware, software, and firmware and/or by human operators.
[0023] If the specification states a component or feature "may", "can", "could", or "might" be included or have a characteristic, that particular component or feature is not
required to be included or have the characteristic.
[0024] Although the present invention has been described with respect to monitoring and surveillance for defense purposes, it should be appreciated that the same has been done merely to illustrate the invention in an exemplary manner and any other purpose or function for which the explained structure or configuration can be used, is covered within the scope of the present disclosure.
[0025] Exemplary embodiments will now be described more fully hereinafter with reference to the accompanying drawings, in which exemplary embodiments are shown. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. These embodiments are provided so that this disclosure will be thorough and complete and will fully convey the scope of the invention to those of ordinary skill in the art. Moreover, all statements herein reciting embodiments of the invention, as well as specific examples thereof, are intended to encompass both structural and functional equivalents thereof. Additionally, it is intended that such equivalents include both currently known equivalents as well as equivalents developed in the future (i.e., any elements developed that perform the same function, regardless of structure).
[00261 The following is a detailed description of embodiments of the disclosure depicted in the accompanying drawings. The embodiments are in such detail as to clearly communicate the disclosure. However, the amount of detail offered is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the present disclosure as defined by the appended claims.
[00271 Each of the appended claims defines a separate invention, which for infringement purposes is recognized as including equivalents to the various elements or limitations specified in the claims. Depending on the context, all references below to the "invention" may in some cases refer to certain specific embodiments only. In other cases, it will be recognized that references to the "invention" will refer to subject matter recited in one or more, but not necessarily all, of the claims.
[0028] Various terms as used herein are shown below. To the extent a term used in a claim is not defined below, it should be given the broadest definition persons in the pertinent art have given that term as reflected in printed publications and issued patents at the time of filing.
[0029] In an embodiment of the present disclosure, FIG. 1 is a block diagram of the system (100) for detecting cyberattack using federated machine learning. The system (100) comprises of plurality of device (102) for managing the production, supply, manufacturing and analysis as well as a cloud server for processing, analyzing (208) and storing a data sent or received from the plurality of device (102). Then the system (100) further comprises of another cloud server (106) which connects all the cloud server and a user interface (108).
[0030] In an aspect of the present invention, a cloud server is a physical or virtual infrastructure that is capable of performing application and information processing storage. The virtual are usually created using the virtualization software. These software divide the physical server into multiple virtual servers. The advantage of using the cloud server is that it is cost-effective, easily scalable, and costs less than the physical server.
[0031] In an aspect of the present invention, the plurality of device (102) installed in the industry can be any machine or equipment capable of connected to each other and the internet.
[00321 In an aspect of the present invention, the plurality of device (102) are smart grids, autonomous transportation devices, gas pipeline devices or any other cyber physical devices.
[0033] In an aspect of the present invention, the user interface (108) can be personal computer, personal digital assistant, tablet device, or smart phone, connected to the cloud server wirelessly. They will enable the controller or the operator who is running the system (100) to know about the system (100) of the system (100) and also get the alert in the case where there is a cyber-attack on the system (100).
[0034] In an aspect of the present invention, the plurality of device (102)managing the production, supply, manufacturing, and analysis is connected to the cloud server. The plurality of cloud server (104)are responsible for processing, analyzing (208) and storing data sent or received from the plurality of device (102). The data from this plurality of device (102) are sent to the plurality of server using wireless technology such as Wi-Fi, Bluetooth or Zigbee. This enables them to and fro transmission of the data between the plurality of cloud and the plurality of device (102).
[0035] In an aspect of the present invention, another cloud server (106) is connected to the plurality of cloud server (104), which enables the easy transmission and sharing of the analysis which was done by the plurality of device (102).
[0036] In another aspect of the present invention, there are two ways of sending data to the cloud first, we can send by using HTTP; also, we can send by using MQTT. We can use the AWS IoT platform to provide secure communication between the IoT devices and the amazon AWS cloud to send and receive data.
[00371 In another aspect of the present invention, Fig. 2 illustrates the method (200) for detecting cyberattack using federated machine learning, wherein said method (200) comprises steps of: deploying (202), by the plurality of cloud server (104), a machine learning model for identifying any cyberattack; collecting (204), by the plurality of device
(102), the data related to the production, supply, manufacturing and analysis in the industries; sending (206), by the plurality of the devices, the data collected to the plurality of cloud server (104)s wirelessly; analyzing (208), by the plurality of cloud server (104)s, the data sent by the plurality of device (102) for any possible cyberattack; alerting (210), by another cloud server (106), by sending the alert to the user interface (108) after analyzing (208) the data sent by the plurality of cloud server (104)s.
[0038] In another aspect of the present invention, for deploying (202) the machine learning model on the plurality of cloud server (104), the training of the model is done on some local system (100). Then the inference logic is wrapped into a flask application. After the wrapping of the inference logic, using docker, the containerization of the flask application is done. Finally, hosting the docker container on an AWS ec2 instance and consuming the webservices.
[0039] In another aspect of the present invention, the plurality of the devices are responsible for collecting (204) industrial data. This industrial data is not in the form of either pictures or videos but only numeric so that it can be easily analyzed by the system (100).
[0040] In another aspect of the present invention, after the collection of the data it is sent to the plurality of cloud server (104) for being analyzed.
[0041] In another aspect of the present invention, the plurality of cloud server (104)is capable of analyzing (208) any possible cyberattack by scanning the vulnerable endpoints. These vulnerable endpoints are the vulnerabilities within the OS, apps, and browsers used to steal the endpoint data. This data can be simply thwarted by requiring the user to authenticate to access their files. This analysis is done by the machine learning model loaded on the plurality of cloud server (104).
[00421 In another aspect of the present invention, in case any vulnerable endpoint is found, the data is shared with every other cloud server using another cloud server (106), which helps machine learning model loaded on other cloud servers to train themselves.
[00431 In another aspect of the present invention, the cloud server can send alert to the user interface (108) after analyzing (208) the data sent by the cloud server. Another cloud server (106) sends the alert to the user interface (108) in the form of a risk report.
[0044] While the foregoing describes, various embodiments of the present invention, other and further embodiments of the invention may be devised without departing from the basic scope thereof. The scope of the invention is determined by the claims that follow. The invention is not limited to the described embodiments, versions or examples, which are included to enable a person having ordinary skill in the art to make and use the invention when combined with information and knowledge available to the person having ordinary skill in the art.
[0045] The present invention is not limited to the above-described specific embodiments, and various modifications and variations are possible. Any modifications, equivalents, improvements and the like made to the above embodiments in accordance with the technical spirit of the present invention should be included in the scope of the present invention.
[00461 Thus, the scope of the present disclosure is defined by the appended claims and includes both combinations and sub-combinations of the various features described hereinabove as well as variations and modifications thereof, which would occur to persons skilled in the art upon reading the foregoing description.
Claims (7)
1. A system (100) for detecting cyberattack using federated machine learning, wherein said system (100) comprises of:
a plurality of device (102) for managing the production, supply, manufacturing and analysis, installed at various industries;
a plurality of cloud server (104), connected to the plurality of device (102), for processing, analyzing and storing a data sent or received from the plurality of device (102);
an another cloud server (106), connected to the plurality of cloud server (104);
a user interface (108), connected to the cloud server, for alerting (210) during a cyberattack.
2. The system (100) for detecting cyberattack using federated machine learning as claimed in claim 1, wherein the plurality of device (102) are smart grids, autonomous transportation devices, gas pipeline devices or any other cyber physical devices.
3. The system (100) for detecting cyberattack using federated machine learning as claimed in claim 1, wherein the plurality of device (102) in the industries are connected with each other through Wi-Fi, Bluetooth or Zigbee for receiving or transmitting the data to-fro from the plurality of cloud server.
4. The system (100) for detecting cyberattack using federated machine learning as claimed in claim 1, wherein the user interface (108) includes a personal computer, personal digital assistant, tablet device, or smart phone, connected to the cloud server wirelessly.
5. A method (200) for detecting cyberattack using federated machine learning, wherein said method (200) comprises steps of:
deploying (202), by the plurality of cloud server (104), a machine learning model for identifying any cyberattack; collecting (204), by the plurality of device (102), the data related to the production, supply, manufacturing and analysis in the industries; sending (206), by the plurality of the devices, the data collected to the plurality of cloud server (104) wirelessly; analyzing (208), by the plurality of cloud server (104), the data sent by the plurality of device (102) for any possible cyberattack; alerting (210), by the another cloud server (106), by sending the alert to the user interface (108) after analyzing (208) the data sent by the plurality of cloud server (104).
6. The method (200) for detecting cyberattack using federated machine learning as claimed in claim 5, wherein the another cloud server (106) sends the alert to the user interface (108) in a form of a risk report.
7. The method (200) for detecting cyberattack using federated machine learning as claimed in claim 5, wherein the data is analyzed by analyzing (208) the vulnerable endpoints.
Application no.: Total no. of sheets: 2 21 Jul 2021 2021104400 Page 1 of 2
FIG. 1. Block diagram of the system for detecting cyberattack using federated machine learning
Application no.: Total no. of sheets: 2 21 Jul 2021 2021104400 Page 2 of 2
FIG. 2 Flowchart of method for detecting cyberattack using federated machine learning
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| AU2021104400A AU2021104400A4 (en) | 2021-07-21 | 2021-07-21 | An intelligent system for detecting behavioral cyber attack on industrial iot using ai federated learning algorithm |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| AU2021104400A AU2021104400A4 (en) | 2021-07-21 | 2021-07-21 | An intelligent system for detecting behavioral cyber attack on industrial iot using ai federated learning algorithm |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| AU2021104400A4 true AU2021104400A4 (en) | 2021-09-16 |
Family
ID=77666598
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| AU2021104400A Ceased AU2021104400A4 (en) | 2021-07-21 | 2021-07-21 | An intelligent system for detecting behavioral cyber attack on industrial iot using ai federated learning algorithm |
Country Status (1)
| Country | Link |
|---|---|
| AU (1) | AU2021104400A4 (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114640498A (en) * | 2022-01-27 | 2022-06-17 | 天津理工大学 | Network intrusion cooperative detection method based on federal learning |
| CN115102763A (en) * | 2022-06-22 | 2022-09-23 | 北京交通大学 | Multi-domain DDoS attack detection method and device based on trusted federal learning |
-
2021
- 2021-07-21 AU AU2021104400A patent/AU2021104400A4/en not_active Ceased
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114640498A (en) * | 2022-01-27 | 2022-06-17 | 天津理工大学 | Network intrusion cooperative detection method based on federal learning |
| CN114640498B (en) * | 2022-01-27 | 2023-08-29 | 天津理工大学 | Network intrusion collaborative detection method based on federal learning |
| CN115102763A (en) * | 2022-06-22 | 2022-09-23 | 北京交通大学 | Multi-domain DDoS attack detection method and device based on trusted federal learning |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US11315024B2 (en) | Cognitive computing systems and services utilizing internet of things environment | |
| AU2021104400A4 (en) | An intelligent system for detecting behavioral cyber attack on industrial iot using ai federated learning algorithm | |
| US11171977B2 (en) | Unsupervised spoofing detection from traffic data in mobile networks | |
| US10051349B2 (en) | Sensor based system and method for premises safety and operational profiling based on drift analysis | |
| CA3115581C (en) | Automated architectural specification generation and hardware identification | |
| KR20170068360A (en) | Storage device for decentralized machine learning and machine learning method thereof | |
| Abualsauod | A hybrid blockchain method in internet of things for privacy and security in unmanned aerial vehicles network | |
| US20170089692A1 (en) | Monitoring of gauges | |
| EP4116906A1 (en) | Method for warehouse storage-location monitoring, computer device, and non-volatile storage medium | |
| US11297564B2 (en) | System and method for assigning dynamic operation of devices in a communication network | |
| CN108510086A (en) | Failure counte-rplan determine method and device | |
| Elgazzar et al. | Revisiting the internet of things: New trends, opportunities and grand challenges | |
| US10693841B2 (en) | System and method for transmitting data relating to an object | |
| US9942262B1 (en) | Cyber-physical system defense | |
| CN116781347A (en) | Industrial Internet of things intrusion detection method and device based on deep learning | |
| US20240153059A1 (en) | Method and system for anomaly detection using multimodal knowledge graph | |
| US20220207863A1 (en) | Object detection device, object detection method, program, and recording medium | |
| Basheer et al. | A secured smart automation system for computer labs in engineering colleges using the internet of things | |
| TWI703846B (en) | URL abnormal location method, device, server and storage medium | |
| CN110955891B (en) | File detection method, device and system and data processing method | |
| DE102022106153A1 (en) | METHODS TO SUPPORT AUTONOMOUS ROBOT-TO-ROBOT IDENTIFICATION AND TASK DELEGATION | |
| KR20220087807A (en) | Transport logistics system and controlling method of the same | |
| Ganesh et al. | The Industry 4.0 for Secure and Smarter Manufacturing | |
| Cuzzocrea et al. | An innovative architecture for supporting cyber-physical security systems | |
| AU2021106062A4 (en) | An intelligent system & method to track and navigate moving objects in a smart environment using iot network |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| FGI | Letters patent sealed or granted (innovation patent) | ||
| MK22 | Patent ceased section 143a(d), or expired - non payment of renewal fee or expiry |