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JSDP 2023, 20(1): 133-144 |
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mohammadi S, khalatbary A, Babagoli M. Propose a meta-heuristic model of intrusion detection using feature selection based on improved gray wolf optimization and random forest. JSDP 2023; 20 (1) : 8
URL: http://jsdp.rcisp.ac.ir/article-1-1218-en.html
URL: http://jsdp.rcisp.ac.ir/article-1-1218-en.html
K.N.Toosi University of Technology
Abstract: (909 Views)
Rapid development in the Internet and communications have led to dramatic growth in computer networks, network size, and data exchange, and this can pose harmful threats to the network. Intrusion detection systems play an important role in the security of Internet networks, which protects the privacy, integrity, and availability of the network by inspecting network traffic. Intrusion detection models in the field of network security are predictive models that are used to predict malicious data in networks and one of the most widely used models in intrusion detection systems is based on machine learning. The imbalance between the accuracy of detection and false alarm rate is one of the most important challenges in this regard. In this paper, meta-heuristic algorithms are used to increase searchability and machine learning method is used to increase computational power and classification. Therefore, in this study, an efficient model based on the gray wolf algorithm and random forest algorithm to identify the best set of traffic features to identify and prevent cyberattacks is presented. The gray wolf algorithm is used to find the best feature subset and the random forest is used to evaluate each subset. This algorithm has also been improved to increase gray wolf performance. The accuracy obtained for correct classification in the proposed method in the NSL-KDD data set. as shown in the result, the detection accuracy of the traditional and improved gray wolf method is obtained 97.14% and 98.97%, respectively, which is outperformed other methods.
Article number: 8
Keywords: Intrusion detection system, Feature Selection, improved Gray Wolf Optimization Algorithm, Random Forest, Machine Learning
Type of Study: Research |
Subject:
Paper
Received: 2021/03/23 | Accepted: 2022/07/31 | Published: 2023/08/13 | ePublished: 2023/08/13
Received: 2021/03/23 | Accepted: 2022/07/31 | Published: 2023/08/13 | ePublished: 2023/08/13
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