Malicious anchor node detection method based on isolated forest and sequential probability ratio test

Abstract

The invention discloses a malicious anchor node detection method based on an isolated forest and a sequential probability ratio test. The malicious anchor node detection method combines an isolated forest algorithm and a voting mechanism to obtain reliable information in anchor nodes, and builds a detection model through the reliable information. According to the malicious anchor node detection method, only a single ranging algorithm is needed for ranging, and multiple ranging algorithms do not need to be used for ranging, and meanwhile, the assumption that one of multiple ranging methods is not attacked completely is avoided, so that the malicious anchor node detection method is more suitable for being used in a real field, that is, it does not need to assume that the malicious anchor node detection method is not attacked completely. Normal samples are screened by utilizing an isolated forest, and reference anchor nodes in the normal samples are screened by utilizing a voting mechanism to realize multiple selection, and the reliability of the reference anchor nodes is ensured, so that the subsequent process of obtaining malicious anchor nodes according to the reference anchor nodes is indirectly ensured; and the difference value information is used for sequential probability ratio inspection, so that the detection of malicious anchor nodes is further improved, and the detection accuracy of the anchor nodes is improved, and the final positioning accuracy of subsequent target nodes is also improved.

Description

technical field [0001] The invention relates to the field of wireless sensor networks, more specifically, to a malicious anchor node detection method based on isolated forest and sequential probability ratio test. Background technique [0002] The positioning algorithm adopted by the positioning system of wireless sensor network (WSN) usually uses the anchor node whose position is known as a reference to estimate the position of the position node, which requires the information provided by the anchor node to be completely reliable. However, due to the openness of WSN itself, nodes are likely to suffer from various attacks during the positioning process. Anchor nodes may be affected by the environment or captured by hostile forces and become malicious anchor nodes, affecting the positioning process. The traditional positioning method aims to improve the positioning accuracy and energy efficiency, and does not consider the situation that the network is attacked. Generally, t...

AI Technical Summary

Problems solved by technology

However, when the proportion of malicious anchor nodes increases, it is likely that the positioning samples participated by malicious anchor nodes fall in the normal samples, so they are misjudged as normal samples, so that some malicious anchor nodes cannot be detected

In addition, existing MNDC algorithms only consider uncoordinated attacks and cannot detect malicious anchor nodes under coordinated attacks

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