考虑时滞影响的无线传感网络蠕虫病毒自适应检测方法  

Adaptive Detection of Worm Viruses in Wireless Sensor Networks Considering Time Delay

作　　者：杨芳 刘全明[2] YANG Fang;LIU Quanming(School of Computer Engineering,Shanxi Vocational University of Engineering Science and Technology,Jinzhong Shanxi 030619,China;School of Computer&Information Technology,Shanxi University,Taiyuan Shanxi 030006,China)

机构地区：[1]山西工程科技职业大学计算机工程学院,山西晋中030619 [2]山西大学计算机与信息技术学院,山西太原030006

出　　处：《传感技术学报》2024年第6期1073-1077,共5页Chinese Journal of Sensors and Actuators

基　　金：山西省科学技术厅科技战略项目(202204031401130)。

摘　　要：无线传感网络中的蠕虫病毒攻击存在着一定的时滞情况,攻击检测难度较大。为了准确检测无线传感网络中的蠕虫病毒,提出一种考虑时滞影响的无线传感网络蠕虫病毒自适应检测方法。采集大量无线传感网络流量数据,对全部数据进行降维处理。在网络蠕虫病毒攻击存在时滞的情况下,将PGM-NMF算法和聚类分析方法相结合,实现无线传感网络节点的异常检测及分类,判断蠕虫攻击的类型,实现蠕虫病毒自适应检测。仿真结果表明:蠕虫病毒攻击时滞为12 ms时,所提方法检测蠕虫病毒的漏报率为9.3%,成功率为95.50%,误报率为0.69%,漏检率为2.9%,蠕虫病毒自适应检测耗时平均值为5.0 s。The worm attacks in wireless sensor networks have certain time delays,and the detection of attacks is difficult.In order to accu-rately detect worms in wireless sensor networks,an adaptive detection method of worms in wireless sensor networks considering the effect of time delay is proposed.A large number of wireless sensor network traffic data are collected,and the dimension of all data is reduced.In the case of delay in network worm attack,PGM-NMF algorithm and clustering analysis method are combined to realize anomaly detec-tion and classification of wireless sensor network nodes,the type of worm attack is judged,and worm virus adaptive detection is realized.The simulation results show that when the delay of worm virus attack is 12 ms,the miss alarm rate of the proposed method is 9.3%,the success rate is 95.50%,the false alarm rate is 0.69%,the missed detection rate is 2.9%,and the average time of worm virus adaptive de-tection is 5.0 s.

关 键 词：无线传感网络 蠕虫病毒 时滞影响 聚类分析 

分 类 号：TP393.08[自动化与计算机技术—计算机应用技术]