An effect of chaos grasshopper optimization algorithm for protection of network infrastructure

Abstract

Due to the proliferation of sophisticated cyber extortion with exponentially critical effects, intrusion detection system is being evolved systematically their revealing, understanding, attribution and mitigation capabilities. Unfortunately, most of the modern Intrusion Detection System (IDS) technique does not provide sufficient defense services in the wireless environment while maintaining operational continuity and the stability of the defense objective in the presence of intruders and modern attacks. To resolve this problem, we propose a new feature selection technique by combining Ensemble of Feature Selection (EFS) and Chaotic Adaptive Grasshopper Optimization Algorithm (CAGOA) method, called ECAGOA. The proposed method has the capability of preventing stagnation issue and is particularly credited to the following three aspects. Firstly, EFS method is applied for selecting the high ranked subset of attributes. Then, we have employed chaos concept in Grasshopper Optimization Algorithm (GOA) which generates a uniformly distributed population to enhance the quality of the initial populations and has the capability to manage two different issues such as the ability to search for new space termed as exploration and the ability to use existing space termed as exploitation in the optimization process. In order to avoid local optima and premature convergence, lastly, an adaptive grasshopper optimization algorithm is developed by using organized parameter adaptation method. Furthermore, the adaptive behavior of GOA is applied to decide whether a record signifies an anomaly or not, differing from some approaches acquainted in the literature. Support vector machine (SVM) is used as a fitness function in the proposed method to choose the relevant features that can help classify the attacks accurately. In addition, it is also applied to optimize the penalty factor (C), kernel parameter (σ), and tube size (ϵ) of SVM method. The proposed algorithm is evaluated using three popular datasets: ISCX 2012, NSL-KDD and CIC-IDS2017. The evaluation results show that the proposed method outperformed several feature selection techniques from state-of-the-art methods in terms of detection rate, accuracy, and false alarm rate.