Preventing and detecting intrusions and attacks on wireless networks has
become an important and serious challenge. On the other hand, due to the
limited resources of wireless nodes, the use of monitoring nodes for permanent
monitoring in wireless sensor networks in order to prevent and detect intrusion
and attacks in this type of network is practically non-existent. Therefore, the
solution to overcome this problem today is the discussion of remote-control
systems and has become one of the topics of interest in various fields. Remote
monitoring of node performance and behavior in wireless sensor networks, in
addition to detecting malicious nodes within the network, can also predict
malicious node behavior in future. In present research, a network intrusion
detection system using feature selection based on a combination of Whale
optimization algorithm (WOA) and genetic algorithm (GA) and sample-based
classification is proposed. In this research, the standard data set KDDCUP1999
has been used in which the characteristics related to healthy nodes and types
of malicious nodes are stored based on the type of attacks in the network. The
proposed method is based on the combination of feature selection based on Whale
optimization algorithm and genetic algorithm with KNN classification in terms
of accuracy criteria, has better results than other previous methods. Based on
this, it can be said that the Whale optimization algorithm and the genetic
algorithm have extracted the features related to the class label well, and the
KNN method has been able to well detect the misconduct nodes in the intrusion
detection data set in wireless networks.

本文提出了一种基于特征选择算法的网络入侵检测系统，该算法采用鲸鱼优化算法和遗传算法来挑选入侵节点特征，并结合 KNN 分类算法进行检测，实验结果表明该方法在准确性方面具有优越性。