Malicious Node Detection Method in Wireless Ad Hoc Networks Based on Behavioral Cognition

Abstract

The invention discloses a behavior cognition-based wireless self-organizing network malicious node detection method, which mainly solves the problem of high false detection rate caused by not considering network congestion in the existing malicious node detection system. Its implementation plan includes: nodes in the network periodically monitor the working status information of neighboring nodes; nodes periodically evaluate the quantitative trust degree of neighboring nodes; second judgments are made on nodes with lower trust degrees based on whether they may be congested within the evaluation period To prevent false detection; calculate direct trust degree; interact with neighboring nodes and calculate recommended trust degree; integrate direct trust degree and recommended trust degree to obtain overall trust degree; judge malicious nodes and isolate them. The invention can reduce the possibility of misjudging normal nodes as malicious nodes, reduce the negative impact of malicious nodes on the network, improve network reliability, and can be used for detection and defense of malicious node attacks in wireless self-organizing networks.

Description

technical field [0001] The invention belongs to the technical field of communication, and in particular relates to a malicious node detection method, which can be used for detecting and defending malicious node attacks in a wireless self-organizing network. Background technique [0002] A wireless ad hoc network is a multi-hop autonomous network formed by independent wireless temporary interconnection of wireless devices that do not depend on any fixed infrastructure. Due to the limited communication range of the nodes, the data transmission in the network needs to rely on cooperation with other intermediate nodes and adopt the method of multi-hop communication. Malicious nodes intruding into the network will attack the network by means of malicious packet loss, intentional delay, false routing, etc., resulting in a serious drop in network throughput or even network paralysis. In order to alleviate the dangers faced by the network, there are many methods based on cryptograp...

AI Technical Summary

Problems solved by technology

However, in a wireless ad-hoc network, due to the changeable network status, in addition to malicious behavior, network congestion, buffer overflow and other reasons may also cause network packet loss or high delay.

Credibility evaluation based only on packet loss rate and delay statistics can easily lead to a decrease in the credibility of non-malicious nodes, causing normal nodes to be misjudged as malicious nodes and move out of the network, which seriously affects the performance of wireless ad hoc networks

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