Metaheuristic Algorithms for Feature Selection in Intrusion Detection Systems: A Systematic Review
Subject Areas : Multimedia Processing, Communications Systems, Intelligent Systemsyashar Pourardebil khah 1 , Mirsaeid Hosseini Shirvani 2 , Homayun Motameni 3
1 - PhD student, Department of Computer Engineering, Sari Branch, Islamic Azad University, Sari, Iran
2 - Assistant Professor, Department of Computer Engineering, Sari Branch, Islamic Azad University, Sari, Iran
3 - Professor, Department of Computer Engineering, Sari Branch, Islamic Azad University, Sari, Iran
Keywords: Feature selection, meta-heuristic algorithm, intrusion detection system, cloud computing, optimization, Network Security,
Abstract :
Introduction: Considering the increasing importance of network security in complex and dynamic environments, intrusion detection systems (IDS) play a very important role in identifying and dealing with security threats. However, the presence of a large amount of data in networks affects the efficiency of intrusion detection systems. Feature selection as a critical step in data preprocessing can help improve the accuracy, speed, and efficiency of these systems. This article deals with the systematic review of feature selection methods based on meta-heuristic algorithms in intrusion detection systems in the cloud computing environment.
Method: The methodology of this article includes a comprehensive review of the research conducted in the field of feature selection for IDS. In this review, meta-heuristic algorithms such as genetic algorithm, particle swarm optimization (PSO), bee colony optimization, bat algorithm, and other nature-inspired optimization methods are thoroughly reviewed. These algorithms are chosen due to their ability to search a large space of features and identify the best combinations to improve the performance of IDSs.
Evaluation: In this paper, detailed comparisons have been made between different algorithms in terms of performance criteria such as detection accuracy, false alarm rate, processing time and the number of selected features. Also, different data sets that have been used in this field have been discussed to evaluate the efficiency of each of the methods in different conditions.
Challenge: In addition, key challenges in this field have been identified and analyzed. These challenges include things such as high computational complexity, the problem of processing overhead in cloud environments, the balance between accuracy and detection speed, and the problem of feature interference. Also, research gaps in this field that require further research have been identified.
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