Research On Instrucsion Detection System Based On Wireless Sensor Networks

Wireless sensor networks(WSNs)have been widely used in various fields in recent years and have important military value and broad commercial prospects.However,due to the limited resources of WSNs' terminal equipment and the space development of wireless communication environment,network intruders can easily eavesdrop,intercept,falsify and tamper with data information.In view of the above,it is very important to design efficient energy-saving intrusion detection models and algorithms.According to the inherent characteristics of WSNs,this dissertation focuses on the research of instrucion detection system(IDS),focuses on data mining and intelligent processing in intrusion detection,proposes a high performance intrusion detection system model suitable for WSNs,And post-test response mechanism,and finally verified by experiments.The main innovations and contents of this paper include:(1)In this paper,we consider the node handling,storage capacity and energy constraints of WSNs,and propose a distributed intrusion detection model based on clustered WSNs.By combining this model and taking into account the WSNs model structure,communication mode,node performance design and implementation of the intrusion detection system based on space-time compression.(2)Aiming at the phenomena of large amount of data in large-scale monitoring applications and taking into account the energy-limited characteristics of nodes in WSNs,a clustering-based energy-optimized spatio-temporal compressed sparse autoencoder(SAE)algorithm.Experimental results show that this algorithm can improve the accuracy of intrusion detection by compressing data.Comparing with the traditional PCA and Autoencoder(AE)compression algorithms,The intrusion detection effect of this model is the best,and the correctness rate and distribution of F1 value increase by 7.84% and 15.3% respectively.(3)In order to transmit the data needed for intrusion detection,a real-time intrusion detection system based on space-time compression intrusion detection system is proposed.This system uses decentralized control mechanism to carry out lightweight intrusion detection before each node transmits,and then determines whether to transmit according to the node judgment result,so as to reduce data transmission energy consumption and complete network attack detection in real time.(4)According to the diversity and few features of network intrusion data,we use the support vector machine(SVM)multi-classification algorithm to accurately detect network intrusion types.According to the characteristics of intrusion data,the traditional binary tree SVMmulti-classification algorithm is improved,and an improved multi-classification algorithm based on improved binary tree(IBT)is proposed.The simulation experiments use NSL-KDD data to compare the IBT with the traditional one aginst all(OAA)and Binary Tree(BT)multi-classification algorithms.IBT is significantly higher than the traditional multi-classification algorithm,BT-SVM,the attack detection rate increased by 8.2%,false alarm rate decreased by 8.7%,while reducing the time complexity decreased by 17%.