CN111464399A - Private network anomaly detection method based on software defined radio - Google Patents
Private network anomaly detection method based on software defined radio Download PDFInfo
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Abstract
The invention discloses a private network anomaly detection method based on software defined radio, and belongs to the technical field of software defined radio private network system safety. The device used by the method comprises a software radio device, a corresponding receiving antenna, a corresponding transmitting antenna, an access layer general computer device, a core layer server and a device operation and maintenance server. The invention classifies the equipment performance data by using the characteristics of different functions of each module of the private network and different performance load differences of general computer equipment caused by different service operation conditions, and simultaneously, by using the correlation among the performance indexes of the computer equipment, performs abnormity judgment on various data through multi-dimensional Gaussian distribution and combining classification results, realizes abnormity detection on the operation condition of the private network system, and can effectively detect the abnormity condition which cannot be detected by the traditional fixed threshold setting mode.
Description
Technical Field
The invention relates to the field of software defined radio, in particular to a private network anomaly detection method in the field of software defined radio.
Background
With the development of new applications such as artificial intelligence, cloud computing, internet of things, big data and the like, a voice-based Terrestrial Trunked Radio (TETRA) and a Police digital trunking system (PDT) are difficult to adapt to the increasing demands and applications of video, picture and the like of the existing trunking communication due to insufficient transmission capacity, L TE (L ong Trunked Evolution) is used as the standard of a 4G wireless communication technology, has the characteristics of flexible bandwidth configuration, low network delay, large user capacity, strong mobile state supporting capacity, high security and high density, and a L TE-based private network system can provide good support for both data service and picture video demands.
However, L TE private networks have a significant disadvantage, and the conventional communication system realizes functions of each module of the communication system by designing a special hardware circuit, which not only has a long development period and a high cost for complex deployment of the system, but also is not beneficial to subsequent system debugging and upgrading, and operations such as subsequent maintenance require professionals familiar with communication protocols such as L TE, private network users lack corresponding professional network managers, and a batch of professional operation and maintenance personnel need to be trained, resulting in higher cost of subsequent operation and maintenance, and the network construction cost of the private network users is limited and cannot be borne at all.
The core idea of Software Defined Radio (SDR) is to build an open, standardized, modular, general-purpose hardware platform. Various communication modules of the method can be realized by software, such as frequency band selection, modulation and demodulation, coding and decoding, communication protocols and the like. The analog-to-digital and digital-to-analog converters are close enough to the antenna to make the SDR system more flexible and open. Therefore, in the process of technology upgrading, people only need to update software and do not need to update hardware, and therefore research and development time and development expenses can be greatly saved. Software defined radio Technology differs from conventional communication equipment development in that a software radio platform is based on Information Technology (IT) Technology, which is based on a general hardware platform, and implements a communication system in software. Compared with the traditional communication system, the software defined radio system has the characteristics of flexibility, configurability, low development cost, easy debugging and easy upgrading of the system and the like.
The private network realized by the SDR and the L TE can meet the requirement of private network communication, and meanwhile, the traditional communication equipment is replaced by the universal hardware platform and the universal computer equipment, so that the overall cost of private network construction is reduced, and the private network system based on the universal equipment also reduces the operation and maintenance threshold of network managers and is convenient for debugging and maintenance of the private network.
Disclosure of Invention
The invention relates to a private network anomaly detection method based on software defined radio, which aims at solving the problems that in the traditional private network construction, a private network-oriented network based on L TE has complex architecture, high networking cost and high maintenance cost, a private network user network has limited construction cost and limited operation and maintenance cost, and a network manager with communication experience is lacked.
In order to achieve the purpose, the invention adopts the following technical scheme:
a special network abnormal detection method based on software defined radio is disclosed, which obtains the performance data of each module general computer device in operation in the special network system, classifies the performance data of each module general computer device by using the difference of the performance data of each module general computer device caused by different realization functions and service operation conditions of each module general computer device, and judges the abnormal of each module general computer device performance data by using the relativity of each module general computer device performance data of CPU load, memory load, disk IO load and network IO through a multidimensional Gaussian distribution model, and combines the classification and judgment results to realize the abnormal detection of the special network system operation condition, the specific steps are as follows:
(1) preparing a data set, acquiring performance data of all module general computer equipment of a private network running in a certain time period, wherein the performance data comprises large private network traffic and performance data in idle, the acquired data comprises CPU load, memory load, disk IO load, network IO and process number, and the performance data of all module general computer equipment is labeled and classified according to different implementation functions and different service running conditions;
(2) performing classification training, namely classifying the prepared data set through a classification algorithm to obtain a trained model;
(3) calculating a multi-dimensional Gaussian distribution probability density function of the classified general computer equipment performance data of each module;
(4) initializing a private network system, and starting a core network and an access network in sequence according to the sequence of a home subscriber server, a mobile management entity, a service gateway, a public data network gateway and an SDR base station;
(5) accessing a private network terminal, registering various terminals inserted with a private network SIM card, accessing the terminals into a private network system and carrying out normal communication;
(6) acquiring real-time data of the performance of each module general computer device when a private network system runs, wherein the real-time data comprises the utilization conditions of CPU load, memory load and disk load, storing and uploading the real-time data to an operation and maintenance server;
(7) comparing the acquired real-time data of the performance of the universal computer equipment of each module with a preset threshold, if certain index data exceeds the threshold, directly outputting the serial number of abnormal data equipment and the abnormal data, and if no index data exceeds the threshold, jumping to the step (8);
(8) classifying the acquired real-time data of the performance of the general computer equipment of each module through the model trained in the step (2), and acquiring the probability that the data belongs to each category;
(9) calculating the probability that the input real-time data of the performance of the universal computer equipment of each module is an abnormal value in the multidimensional Gaussian distribution model of each category obtained in the step (3);
(10) weight superposition, namely performing weight superposition on the probability that the data obtained in the step (8) belongs to each category and the probability that the input real-time data of the performance of the general computer equipment of each module obtained in the step (9) is an abnormal value in the multidimensional Gaussian distribution model of each category, and calculating the probability that the input real-time data of the performance of the general computer equipment of each module is abnormal, namely the abnormal probability of the operation of the private network;
(11) and comparing the obtained abnormal operation probability of the private network with a preset abnormal probability threshold, if the abnormal operation probability of the private network is less than or equal to the threshold, considering that the private network operates normally, and if the abnormal operation probability of the private network is greater than the threshold, considering that the private network operates abnormally, and outputting abnormal equipment numbers and abnormal performance data.
Particularly, the data acquisition interval is set to be once per minute, and the acquired data is monitored in real time while the data is acquired per minute after the system is operated, so that the condition that the operation condition of any one device is detected abnormally every minute is ensured.
The method has the beneficial effects that the abnormal conditions which cannot be detected by the traditional method are judged through the multidimensional Gaussian distribution model by utilizing the correlation among the performance data. The method is different from the traditional operation and maintenance method in that the abnormal alarm is started when the upper and lower limit thresholds are fixed and exceed the threshold, and the abnormal alarm accuracy is higher. Meanwhile, the problem that the distribution difference of computer performance data caused by different services cannot be uniformly detected by using one multi-dimensional Gaussian distribution is solved through a classification algorithm. The collected unknown data which needs to detect whether the equipment is abnormal or not is firstly classified, the probabilities of all types are predicted, the probabilities are taken as weights, and the probabilities of the unknown data which are abnormal in various Gaussian distribution models are subjected to weighted accumulation, so that the result is more accurate, the unified model for detecting the abnormality of all the general computer equipment is realized, and the working difficulty of operation and maintenance personnel is greatly reduced.
Drawings
FIG. 1 is an overall structure diagram of the SDR platform-based private network operation anomaly detection system of the present invention;
fig. 2 is an overall flow chart of the method for detecting the abnormal operation of the private network based on the SDR platform.
Detailed Description
In order to more clearly describe the technical contents of the present invention, the present invention will be further described with reference to the accompanying drawings.
The main flow chart of the special network operation abnormity detection method based on the SDR platform is shown in figure 1. The method comprises the following specific steps:
(1) preparing a data set, acquiring performance data of all module general computer equipment of a private network running in a certain time period, wherein the performance data comprises large private network traffic and performance data in idle, the acquired data comprises CPU load, memory load, disk IO load, network IO and process number, and the performance data of all module general computer equipment is labeled and classified according to different implementation functions and different service running conditions;
(2) performing classification training, namely classifying the prepared data set through a classification algorithm to obtain a trained model;
(3) calculating a multi-dimensional Gaussian distribution probability density function of the classified general computer equipment performance data of each module;
(4) starting a home subscriber server, and confirming that the information in the security authentication certificate and the MySQ L database comprises a user imsi code, a mmeidenitity, a host name and the like is correct;
(5) the mobile management entity starts to confirm that the connection with the home subscriber server is normal through an S6a interface;
(6) starting a service gateway and a public data network gateway, confirming that a GTP protocol is normally started, normally connecting with an MME and normally operating a core network;
(7) and confirming and configuring parameters such as uplink and downlink frequency bands, gain and the like of the access layer base station, and normally communicating with the core network after starting.
(8) Various private network terminals are accessed through a preset SIM card which is consistent with user information in an HSS database, and data are normally transmitted;
(9) acquiring real-time data of the performance of each module general computer device when a private network system runs, wherein the real-time data comprises the utilization conditions of CPU load, memory load and disk load, storing and uploading the real-time data to an operation and maintenance server;
(10) comparing the acquired real-time data of the performance of the universal computer equipment of each module with a preset threshold, if certain index data exceeds the threshold, directly outputting the serial number of abnormal data equipment and the abnormal data, and if no index data exceeds the threshold, jumping to the step (11);
(11) classifying the acquired real-time data of the performance of the general computer equipment of each module through the model trained in the step (2), and acquiring the probability P of the data belonging to each classi；
(12) Calculating the probability P of abnormal values in various multidimensional Gaussian distribution function models obtained in the step (3) of the input real-time data of the performance of the universal computer equipment of each modulej。
(13) And (3) weight superposition, namely, carrying out weight superposition on the probability that the data obtained in the step (8) belongs to each category and the probability that the input real-time data of the performance of the general computer equipment of each module is abnormal in the multidimensional Gaussian distribution model of each category, which is obtained in the step (9), so as to obtain the probability that the input real-time data of the performance of the general computer equipment of each module is abnormal
The abnormal operation probability of the private network is obtained;
(14) and comparing the calculated abnormal operation probability P of the equipment with a preset abnormal probability threshold, if the abnormal operation probability P is less than or equal to the preset abnormal probability threshold, the equipment operates normally, and if the abnormal operation probability P is greater than the preset abnormal probability threshold, outputting the number and the performance data of the abnormal equipment.
Fig. 2 shows the overall architecture of the system of the present invention, and the whole system is divided into three parts, namely, a private network core layer, a private network access layer, and a private network terminal layer. The specific functions of each layer are as follows:
(1) the private network core layer is a core control layer of the system, and mainly includes an operation and maintenance Data Server, a Home Subscriber Server (HSS), a Mobility Management Entity (MME), a Serving GateWay (SGW), and a Public Data network GateWay (PGW). The operation and maintenance data server is responsible for storing performance data of all general equipment in the network, classifying the performance data according to different running services of all the equipment, preprocessing various data and then performing anomaly detection by using a multi-dimensional Gaussian distribution model. The HSS is primarily responsible for storing databases of user information, including user profiles, performing authentication and authorization of users, and may provide information about the physical location of users. The MME is a signaling entity and is responsible for a signaling processing part and mainly responsible for mobility management, and access control comprises user authentication, security and permission control, selection of SGW and PGW, attachment and detachment of a terminal, a session management function and the like. The PGW is responsible for a user plane function of forwarding user data, that is, forwarding received user data to the PGW. The PGW is responsible for session and bearer management, and allocates an IP address to an accessed user, and then the transmission of data packets is performed under the IP address.
(2) The private network access layer is mainly responsible for wireless resource management, provides wireless bearing control, access control, uplink and downlink resource dynamic allocation scheduling and the like for the terminal, and schedules and sends paging messages initiated by a mobility management entity and routes user plane data to corresponding service gateways.
(3) The private network terminal layer is positioned at the bottom end of the system and can support wireless channels to access to private network terminals, commercial terminals, cameras, notebook computers and other equipment.
The invention realizes the small-sized lightweight L TE system facing the private network on an open source software radio platform and a general hardware platform, adopts a maintenance mode different from the traditional communication system, adopts IT operation and maintenance technology, maintains cost and meets the practical requirements of private network users.
The universal hardware platform is USRP equipment and is connected with an access layer universal computer through a USB3.0 interface, and the USRP needs to be externally connected with a 5-12V direct-current power supply for supplying power when realizing L TE special network;
the access layer general computer has the requirement on the real-time performance of baseband signal processing, the CPU performance is not lower than i5 three-generation performance, the CPU physical core is four cores or more, the system version is L inux Ubuntu 16.04L TS, and the L inux kernel version is 4.8 or higher;
the access layer computer is responsible for processing baseband signal data, and the USRP is responsible for converting the baseband signal into a radio frequency signal through the processes of frequency conversion, digital-to-analog conversion, filter filtering, crystal oscillator, amplifier and the like; otherwise, the received radio frequency signal can be converted into a baseband signal, and the baseband signal is processed by the general-purpose computer;
the L TE private network system is established on an open-source SDR platform, the basic functions of L TE networks such as EPC, eNB, UE and the like can be realized by adopting an open-source SDR L TE technical framework of OAI, and programs in the OAI are interacted with the USRP through a UHD driver;
the SDR L TE platform adopts OAI develoop branch, EPC part branch is hard 67180ca07c, eNB part branch is hard 7580d021 d;
the system is built on a Software Defined Radio (SDR) L TE platform, and is characterized in that all general computer equipment performance data during normal operation of the system are collected, uploaded and stored in an equipment operation and maintenance server database.
The data set is performance data of a general computer of each module of a private network in normal operation in a certain time period, the performance data of the equipment is classified according to the characteristics of performance difference of the general computer caused by different implementation functions and service operation conditions of each module of the private network, and the collected performance data is marked. Meanwhile, various classified data are preprocessed into a multi-dimensional Gaussian distribution form, and parameters such as a probability density function, a covariance matrix and the like of the data are obtained.
Although the embodiments of the present invention have been described with reference to the accompanying drawings, it is not intended to limit the scope of the invention, and it should be understood by those skilled in the art that various modifications and variations can be made without inventive effort by those skilled in the art.
Claims (2)
1. A special network abnormal detection method based on software defined radio is disclosed, which obtains the performance data of each module general computer device in operation in the special network system, classifies the performance data of each module general computer device by using the difference of the performance data of each module general computer device caused by different realization functions and service operation conditions of each module general computer device, and judges the abnormal of each module general computer device performance data by using the relativity of each module general computer device performance data of CPU load, memory load, disk IO load and network IO through a multidimensional Gaussian distribution model, and combines the classification and judgment results to realize the abnormal detection of the special network system operation condition, the specific steps are as follows:
(1) preparing a data set, acquiring performance data of all module general computer equipment of a private network running in a certain time period, wherein the performance data comprises large private network traffic and performance data in idle, the acquired data comprises CPU load, memory load, disk IO load, network IO and process number, and the performance data of all module general computer equipment is labeled and classified according to different implementation functions and different service running conditions;
(2) performing classification training, namely classifying the prepared data set through a classification algorithm to obtain a trained model;
(3) calculating a multi-dimensional Gaussian distribution probability density function of the classified general computer equipment performance data of each module;
(4) initializing a private network system, and starting a core network and an access network in sequence according to the sequence of a home subscriber server, a mobile management entity, a service gateway, a public data network gateway and an SDR base station;
(5) accessing a private network terminal, registering various terminals inserted with a private network SIM card, accessing the terminals into a private network system and carrying out normal communication;
(6) acquiring real-time data of the performance of each module general computer device when a private network system runs, wherein the real-time data comprises the utilization conditions of CPU load, memory load and disk load, storing and uploading the real-time data to an operation and maintenance server;
(7) comparing the acquired real-time data of the performance of the universal computer equipment of each module with a preset threshold, if certain index data exceeds the threshold, directly outputting the serial number of abnormal data equipment and the abnormal data, and if no index data exceeds the threshold, jumping to the step (8);
(8) classifying the acquired real-time data of the performance of the general computer equipment of each module through the model trained in the step (2), and acquiring the probability that the data belongs to each category;
(9) calculating the probability that the input real-time data of the performance of the universal computer equipment of each module is an abnormal value in the multidimensional Gaussian distribution model of each category obtained in the step (3);
(10) weight superposition, namely performing weight superposition on the probability that the data obtained in the step (8) belongs to each category and the probability that the input real-time data of the performance of the general computer equipment of each module obtained in the step (9) is an abnormal value in the multidimensional Gaussian distribution model of each category, and calculating the probability that the input real-time data of the performance of the general computer equipment of each module is abnormal, namely the abnormal probability of the operation of the private network;
(11) and comparing the obtained abnormal operation probability of the private network with a preset abnormal probability threshold, if the abnormal operation probability of the private network is less than or equal to the threshold, considering that the private network operates normally, and if the abnormal operation probability of the private network is greater than the threshold, considering that the private network operates abnormally, and outputting abnormal equipment numbers and abnormal performance data.
2. The private network anomaly detection method based on software defined radio according to claim 1, characterized in that: the data acquisition interval is set to be once per minute, and the system monitors the acquired data in real time while acquiring the data per minute after running so as to ensure that the running condition of any equipment is subjected to abnormal detection every minute.
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