Wireless Network Safety Status Prediction Based on Fuzzy Logic

Abstract

In WN environment, network safety status means to state of information managing in various WN environments, WN transmission information safety status assessment is one of primary research directions in this territory. Existing network safety status awareness methods are difficult to adapt to real-time changes of network structure, and WN environment is complex and changeable, and they can only analyze the current network safety status, and it is difficult to predict and analyze overall tendency of WN safety status. In order to resist potential attacks, evaluate safety of network and detect attack means in network in a timely manner, this paper introduces fuzzy logic to propose a safety status prediction model for wireless sensor networks, which can help administrators to timely perceive and comprehensively grasp the real-time status of network and predict future advancement direction. In order to assess current network status, a safety status evaluation model for wireless network (WN) depended on fuzzy logic is presented. In cluster head node, neighborhood rough set is used for feature extraction to reduce energy consumption of redundant data on the node. Balance data by synthesizing a few over-sampling techniques, and then use random forest to detect attacks on the network to identify attack types. Combined with the status element acquisition mechanism, three status indicators, namely attack frequency, total number of attacks and threat factor, are extracted. According to the status indicators and status calculation method, the network safety status value is calculated, and current network safety status is evaluated by referring to network safety level divided by National Internet Emergency Response Center. Neighborhood rough set is applied to complete attribute reduction, which can effectively deal with underwater mixed data and obtain feature subsets with same classification capability as initial data. Safety status of WSN is predicted based on random forest. The risk degree of WN status is divided into fuzzy subsets, and the process of dynamic prediction of safety status is designed. Based on test values, highest input signal spectrum of the system is 30 mV, and the lowest input signal spectrum is -15 mV, which is consistent with the selected 120 groups of status data sequence diagram, the fluctuation amplitude of the input signal under 40~62 groups of samples is small, basically unchanged, consistent with the selected 120 groups of status data sequence diagram. Fuzzy logic model represented by star broken line has higher precision than decision tree and the limit learning machine in all five different attack types. mapped network safety status grade can also effectively express the actual network safety status. indicating that the prediction results of the system are accurate.