论文标题

使用遗传鲸鱼优化算法和基于样本的分类,基于特征选择的入侵检测系统

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

论文作者

Mojtahedi, Amir, Sorouri, Farid, Souha, Alireza Najafi, Molazadeh, Aidin, Mehr, Saeedeh Shafaei

论文摘要

防止和检测对无线网络的入侵和攻击已成为一个重要而严重的挑战。另一方面,由于无线节点的资源有限,因此在无线传感器网络中使用监视节点进行永久监视以防止和检测这种网络中的入侵和攻击实际上是不存在的。因此,今天要克服这个问题的解决方案是对遥控系统的讨论,并已成为各个领域感兴趣的主题之一。除了检测网络中的恶意节点外,对无线传感器网络中节点性能和行为的远程监视还可以预测将来的恶意节点行为。在目前的研究中,提出了基于鲸鱼优化算法(WOA)和遗传算法(GA)和基于样本分类的组合使用特征选择的网络入侵检测系统。在这项研究中,已使用标准数据集KDDCUP1999,其中与健康节点和恶意节点类型相关的特征根据网络中的攻击类型存储。所提出的方法基于基于鲸鱼优化算法和遗传算法与KNN分类的特征选择的组合,其精度标准的结果比以前的其他方法更好。基于此,可以说,鲸鱼优化算法和遗传算法已经很好地提取了与类标签相关的特征,并且KNN方法已经能够很好地检测到无线网络中入侵检测数据集中的不当行为节点。

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.

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