Rabbit and Tortoise Optimization Algorithm with Mutual Information Based Adaptive Strategy for Network Intrusion Detection

In the modern era of highly interconnectedness, data and information are constantly transmitted over networks. Ensuring the security of confidential information and protecting computer systems from network threats has become very important. Therefore, it is important to develop an effective network...

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Vydáno v:	Programming and computer software Ročník 51; číslo 6; s. 359 - 372
Hlavní autoři:	Bhuvaneswari, T., Soundar, K. Ruba, Sekar, R. Chandra Guru
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Moscow Pleiades Publishing 01.12.2025 Springer Nature B.V
Témata:	Accuracy Algorithms Artificial Intelligence Classification Computer Science Cybersecurity Datasets Feature selection Intrusion detection systems Machine learning Methods Missing data Neural networks Operating Systems Optimization Optimization algorithms Optimization techniques Software Engineering Software Engineering/Programming and Operating Systems Support vector machines wrapper based feature selection rabbit and tortoise optimization algorithm (RTOA) mutual information (MI) metaheuristic optimization algorithm network intrusion detection system (NIDS) feature selection (FS)
ISSN:	0361-7688, 1608-3261
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Abstract	In the modern era of highly interconnectedness, data and information are constantly transmitted over networks. Ensuring the security of confidential information and protecting computer systems from network threats has become very important. Therefore, it is important to develop an effective network intrusion detection system (NIDS) using optimal features. These optimal features can be identified through computational intelligence by learning patterns and relationships among features using machine learning techniques. This paper presents a rabbit and tortoise optimization technique for selecting optimal features. For evaluation, the UNSW-NB15 dataset is utilized. The optimization results achieve an accuracy of 94.12% for binary classification and 93.92% for multiclass classification, with 26 optimal features selected from the entire feature set. To improve the approach, an adaptive strategy based on mutual information is used to control the number of optimal features. This strategy, together with the Rabbit and Tortoise algorithm, improves the accuracy, showing 94.69% for binary classification and 94.03% for multiclass classification, while reducing the number of selected features to 9 only. The comparative performance analysis shows that the proposed feature selection method outperforms other state-of-the-art methods, providing more accurate and reliable results in identifying cyber threats. In addition, the relationship plot between the number of optimal features and the accuracy of the model shows that selecting only 9 features is effective in achieving high accuracy in detecting and predicting cyber attacks.
AbstractList	In the modern era of highly interconnectedness, data and information are constantly transmitted over networks. Ensuring the security of confidential information and protecting computer systems from network threats has become very important. Therefore, it is important to develop an effective network intrusion detection system (NIDS) using optimal features. These optimal features can be identified through computational intelligence by learning patterns and relationships among features using machine learning techniques. This paper presents a rabbit and tortoise optimization technique for selecting optimal features. For evaluation, the UNSW-NB15 dataset is utilized. The optimization results achieve an accuracy of 94.12% for binary classification and 93.92% for multiclass classification, with 26 optimal features selected from the entire feature set. To improve the approach, an adaptive strategy based on mutual information is used to control the number of optimal features. This strategy, together with the Rabbit and Tortoise algorithm, improves the accuracy, showing 94.69% for binary classification and 94.03% for multiclass classification, while reducing the number of selected features to 9 only. The comparative performance analysis shows that the proposed feature selection method outperforms other state-of-the-art methods, providing more accurate and reliable results in identifying cyber threats. In addition, the relationship plot between the number of optimal features and the accuracy of the model shows that selecting only 9 features is effective in achieving high accuracy in detecting and predicting cyber attacks.
Author	Bhuvaneswari, T. Soundar, K. Ruba Sekar, R. Chandra Guru
Author_xml	– sequence: 1 givenname: T. surname: Bhuvaneswari fullname: Bhuvaneswari, T. email: bhuvanait2011@gmail.com organization: Department of Computer Science and Engineering, Mepco Schlenk Engineering College – sequence: 2 givenname: K. Ruba surname: Soundar fullname: Soundar, K. Ruba organization: Department of Computer Science and Engineering, Mepco Schlenk Engineering College – sequence: 3 givenname: R. Chandra Guru surname: Sekar fullname: Sekar, R. Chandra Guru organization: Department of Mathematics, Mepco Schlenk Engineering College
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Copyright	Pleiades Publishing, Ltd. 2025 ISSN 0361-7688, Programming and Computer Software, 2025, Vol. 51, No. 6, pp. 359–372. © Pleiades Publishing, Ltd., 2025.Russian Text © The Author(s), 2025, published in Proceedings of ISP RAS, 2025, Vol. 37, No. 4. Pleiades Publishing, Ltd. 2025.
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SubjectTerms	Accuracy Algorithms Artificial Intelligence Classification Computer Science Cybersecurity Datasets Feature selection Intrusion detection systems Machine learning Methods Missing data Neural networks Operating Systems Optimization Optimization algorithms Optimization techniques Software Engineering Software Engineering/Programming and Operating Systems Support vector machines
Title	Rabbit and Tortoise Optimization Algorithm with Mutual Information Based Adaptive Strategy for Network Intrusion Detection
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