基于人工智能的分布式入侵检测研究

doi:10.11896/jsjkx.220700095

计算机科学 ›› 2022, Vol. 49 ›› Issue (10): 353-357.doi: 10.11896/jsjkx.220700095

• 信息安全 • 上一篇

基于人工智能的分布式入侵检测研究

王璐¹, 文武松²

1 重庆第二师范学院人工智能学院重庆 400065
2 清华大学电机系北京 100084

收稿日期:2022-06-11 修回日期:2022-07-25 出版日期:2022-10-15 发布日期:2022-10-13
通讯作者: 王璐(wanglu514@163.com)
基金资助:
重庆市教委科学技术研究项目(KJQN201901607)

Study on Distributed Intrusion Detection System Based on Artificial Intelligence

WANG Lu¹, WEN Wu-song²

1 School of Artificial Intelligence,Chongqing University of Education,Chongqing 400065,China
2 Department of Electrical Engineering,Tsinghua University,Beijing 100084,China

Received:2022-06-11 Revised:2022-07-25 Online:2022-10-15 Published:2022-10-13
About author:WANG Lu,born in 1980,master,asso-ciate professor.Her main research in-terests include artificial intelligence,power electronics and control enginee-ring.
Supported by:
Science and Technology Research Program of Chongqing Municipal Education Commission of China(KJQN201901607).

摘要/Abstract

摘要： 为了解决目前动态加载系统存在的数据处理缺陷以及系统入侵精确度低等问题,以“人工智能技术”应用为例,设计一款功能完善、实用性强的分布式入侵检测系统。首先,在完成系统架构设计和系统数据库设计的基础上,对控制中心、分区控制中心延长网络主机进行全面分析;其次,严格按照响应库相关的响应规则,制定相应的响应对策;然后,借助通信模块判断其入侵行为是否出现异常问题;再次,利用S5720S-28P-SI-AC24口核心交换机对相关数据进行交换处理;接着,选用型号为AD2032的报警响应器对外来入侵行为进行全面监视;另外,在全面分析主体通信实现方式的基础上,利用Libpcap库函数完成对入侵检测流程的科学设计;最后,从环境与参数设置、系统测试结果与分析两个方面入手,对系统性能进行全面测试。结果表明,在人工智能技术的应用背景下,所设计的分布式入侵检测系统可以获得较高的检测精确度,达到了99%,为后期安全、稳定地使用网络提供重要的平台支持。

关键词: 人工智能, 分布式, 入侵检测系统, 设计, 实现

Abstract: In order to solve the problems of data processing defects and low system intrusion accuracy existing in the current dynamic loading system,a distributed intrusion detection system with complete functions and strong practicability is designed by taking the application of “artificial intelligence technology” as an example.Firstly,on the basis of completing the system architecture and database design,comprehensively analyze the control center and the extended network host of the subregional control center,and then formulate corresponding response countermeasures in strict accordance with the relevant response rules of the response library.Secondly,through the use of the communication module,the intrusion behavior is judged to determine whether the intrusion behavior is abnormal.Again,use the S5720S-28P-SI-AC24-port core switch to exchange related data.Then,through the selection of AD2032 alarm responder,a comprehensive monitoring of external intrusion behavior is carried out.In addition,based on the comprehensive analysis of the main body communication implementation,the Libpcap library function is used to complete the scientific design of the intrusion detection process test.The results show that,under the application background of artificial intelligence technology,the distributed intrusion detection system designed in this paper can obtain high detection accuracy,and its accuracy reaches 99%,which provides an important platform for the later security and stable use of the network support.

Key words: Artificial intelligence, Distributed, Intrusion detection system, Design, Implementation

中图分类号:

TP393

王璐, 文武松. 基于人工智能的分布式入侵检测研究[J]. 计算机科学, 2022, 49(10): 353-357. https://doi.org/10.11896/jsjkx.220700095

WANG Lu, WEN Wu-song. Study on Distributed Intrusion Detection System Based on Artificial Intelligence[J]. Computer Science, 2022, 49(10): 353-357. https://doi.org/10.11896/jsjkx.220700095

参考文献

[1]ALLADI T,KOHLI V,CHAMOLA V,et al.Artificial Intelligence(AI)-Empowered Intrusion Detection Architecture for the Internet of Vehicles[J].IEEE Wireless Communications,2021,28(3):144-149.
[2]ZEBIN T,REZVY S,LUO Y,An Explainable AI-Based Intrusion Detection System for DNS Over HTTPS(DoH) Attacks[J].IEEE Transactions on Information Forensics and Security,2022,17:2339-2349.
[3]CHEN X A.Research on the intrusion detection system of computer network[J].Electronic Test,2021(18):76-77,73.
[4]ZHONG W,YU N C.Applying big data based deep learning system to intrusion detection[J].Big Data Mining and Analy-tics,2020,3(3):181-195.
[5]LU L,SUN Y E,HUANG H,et al.Detection of persistent elements in distributed monitoring system[J].Journal of Computer Research and Development,2020,57(5):1046-1056.
[6]TANJ A,GUAN J F.Distributed intrusion detection system of networks based on artificial bee colony algorithm[J].Computer Applications and Software,2019,36(3):326-333.
[7]CHENG W Z,ZHANG L.Talking about distributed intrusion detection system[J].Sci-Tech & Development of Enterprise,2018(7):93-94.
[8]HONG B,CAO Z J.Design and implement of distributed intrusion detection system based on Hadoop[J].Journal of Xi'an Technological University,2018,38(4):390-395,407.
[9]WANG X Y.Design of temporal sequence association rule based intrusion detection behavior detection system for distributed network[J].Modern Electronics Technique,2018,41(3):107-110.
[10]LI H.Research on hybrid architecture for distributed intrusion detection system in wireless network[J].Techniques of Automation and Applications,2018,37(5):52-55,60.
[11]CHOI I,LEE J,KWON T,et al.An Easy-to-use Framework to Build and Operate AI-based Intrusion Detection for In-situ Monitoring[C]//2021 16th Asia Joint Conference on Information Security(AsiaJCIS).2021:1-8.
[12]ALI M,HU Y F,LUONG D K,et al.Adversarial Attacks on AI based Intrusion Detection System for Heterogeneous Wireless Communications Networks[C]//2020 AIAA/IEEE 39th Digital Avionics Systems Conference(DASC).2020:1-6.
[13]LI X.Research and implementation of intrusion detection system based on spark [D].Taiyuan:Shanxi University,2021.
[14]ZHANG S S.Design and implementation of security intrusion detection system based on software definition [D].Hangzhou:Zhejiang University,2020.
[15]HU B.Distributed vulnerability emergency detection system[D].Chengdu:University of Electronic Science and Technology,2020.
[16]GAO Y,LIU Y,JIN Y,et al.A Novel Semi-Supervised Learning Approach for Network Intrusion Detection on Cloud-Based Robotic System[J].IEEE Access,2018,6:50927-50938.
[17]ZHANG W X.Design and implementation of intrusion detection system based on improved cann algorithm [D].Xi'an:Xi'an University of Electronic Science and Technology,2019.
[18]LI J,ZHAO Z,LI R,et al.AI-Based Two-Stage Intrusion Detection for Software Defined IoT Networks[J].IEEE Internet of Things Journal,2019,6(2):2093-2102.
[19]HE J P,LUO L,XIAO K,et al.Framework intrusion detection system based on feature distribution and AI[J].Application Research of Computers,2021,38(9):2746-2751.

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基于人工智能的分布式入侵检测研究

Study on Distributed Intrusion Detection System Based on Artificial Intelligence

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