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Preventing and detecting intrusions and attacks on wireless networks hasbecome an important and serious challenge. On the other hand, due to thelimited resources of wireless nodes, the use of monitoring nodes for permanentmonitoring in wireless sensor networks in order to prevent and detect intrusionand attacks in this type of network is practically non-existent. Therefore, thesolution to overcome this problem today is the discussion of remote-controlsystems and has become one of the topics of interest in various fields. Remotemonitoring of node performance and behavior in wireless sensor networks, inaddition to detecting malicious nodes within the network, can also predictmalicious node behavior in future. In present research, a network intrusiondetection system using feature selection based on a combination of Whaleoptimization algorithm (WOA) and genetic algorithm (GA) and sample-basedclassification is proposed. In this research, the standard data set KDDCUP1999has been used in which the characteristics related to healthy nodes and typesof malicious nodes are stored based on the type of attacks in the network. Theproposed method is based on the combination of feature selection based on Whaleoptimization algorithm and genetic algorithm with KNN classification in termsof accuracy criteria, has better results than other previous methods. Based onthis, it can be said that the Whale optimization algorithm and the geneticalgorithm have extracted the features related to the class label well, and theKNN method has been able to well detect the misconduct nodes in the intrusiondetection data set in wireless networks.

Feature Selection-based Intrusion Detection System Using Genetic Whale Optimization Algorithm and Sample-based Classification