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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Bibliographic Details
Published in:Programming and computer software Vol. 51; no. 6; pp. 359 - 372
Main Authors: Bhuvaneswari, T., Soundar, K. Ruba, Sekar, R. Chandra Guru
Format: Journal Article
Language:English
Published: Moscow Pleiades Publishing 01.12.2025
Springer Nature B.V
Subjects:
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
Online Access:Get full text
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Summary: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.
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ISSN:0361-7688
1608-3261
DOI:10.1134/S0361768825700239