Wireless sensor network intrusion detection method based on integrated learning

Abstract

The invention provides a wireless sensor network intrusion detection method based on integrated learning, and belongs to the technical field of communication. The method comprises the steps of collecting data of each node in a wireless sensor network, preprocessing the data, extracting a feature set of each node, and converting symbol features into values; normalizing each feature value; using a feature selection algorithm to screen out the optimal feature set from the preprocessed feature sets; using an improved support vector machine (SVM) algorithm to serve as a weak classifier, and combingwith the screened out optimal feature training set for training; and using an Adaboost integrated learning algorithm to combine the trained weak classifiers together to form a strong classifier, thenusing the trained strong classifier to test actual data, and picking out normal nodes and abnormal nodes of the wireless sensor network. According to the method provided by the invention, the accuracy of detection on intrusion attacks occurring in the wireless sensor network can be improved, the certain cost of labeled samples is reduced, and intrusion detection and training time is reduced, andthe reliability of the intrusion detection system is enhanced.

Description

technical field [0001] The invention belongs to the technical field of communication, and relates to an intrusion detection method of a wireless sensor network based on integrated learning. Background technique [0002] Wireless Sensor Networks (WSN) is a distributed sensing detection system composed of many sensor nodes deployed in a wide area. The sensor nodes perceive and monitor the information in the deployment area, and transmit data through multi-hop routing. to the cluster head, and forwarded to the base station through the cluster head or forwarded to the base station through other cluster heads to realize data collection and task detection. Due to the characteristics of low node cost, simple deployment, no need for infrastructure, strong invulnerability, and strong adaptability to highly dynamic network topologies, wireless sensor networks are widely used in vehicle monitoring, environmental monitoring, intelligent transportation, intelligent medical care, intellig...

AI Technical Summary

Problems solved by technology

[0004] Since the sensor node deployment algorithm will be limited by the computing power of the node, if you use algorithms and models that are computationally complex and require a large amount of data for training, it will block the normal work of SN or CH, increase the performance of SN or CH, and cause SN or CH inventory cycle reduction

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