针对开放式WSN连接到互联网上的智能设备数量和多样性迅速增加而导致的入侵检测误报和入侵检测准确性等问题，提出一种基于增强型支持向量机(Enhanced Support Vector Machine，ESVM)分类和遗传算法(Genetic Algorithm，GA)特征选择的智能轻量级物联网入侵检测算法。该算法进行预处理以将入侵数据集的复杂流量转换为SVM的可读格式，采用交叉和变异算子智能选择信息量最大的流量特征以降低无线网络流量的维数，使用ESVM算法执行分类以更有效地识别入侵攻击检测。实现结果表明，该算法在选择最优流量和提高检测精度方面均有明显改善。
RESEARCH ON WSN SECURITY BASED ON ENHANCED SUPPORT VECTOR MACHINE AND GENETIC ALGORITHM
To solve the problem of intrusion detection false positives and intrusion detection accuracy caused by the rapid increase in the number and diversity of smart devices connected to the Internet in open WSN,an intelligent lightweight intrusion detection algorithm based on enhanced support vector machine(ESVM)for classification and genetic algorithm(GA)for feature selection is proposed.The algorithm transformed the complex traffic of intrusion data set into the readable format of SVM,and used crossover and mutation operators to intelligently select the traffic characteristics with the largest amount of information to reduce the dimension of wireless network traffic.It used ESVM to perform classification to identify intrusion detection more effectively.The implementation results show that the algorithm has obvious improvement in selecting the optimal flow and improving the detection accuracy.
Enhanced support vector machineGenetic algorithmInternet of thingsLightweight intrusion detection system
万方数据
维普
