Machine Learning approaches in IDS

Md Boktiar Hossain 1, * and Khandoker Hoque 2

1 Department of Information and Communication Engineering, University of Rajshahi, Rajshahi 6205, Bangladesh.
2 Department of Electrical and Electronic Engineering, Brac University, Dhaka, Bangladesh.
 
Review
International Journal of Science and Research Archive, 2022, 07(02), 706-715.
Article DOI: 10.30574/ijsra.2022.7.2.0303
Publication history: 
Received on 18 November 2022; revised on 22 December 2022; accepted on 28 December 2022
 
Abstract: 
With the rapid expansion of digital infrastructures, cybersecurity threats have become increasingly sophisticated, necessitating advanced protection mechanisms. Traditional security solutions, such as firewalls and rule-based intrusion detection systems (IDS), often fail to detect evolving attack patterns. Machine Learning (ML) has emerged as promising approaches for enhancing IDS capabilities by identifying anomalies and predicting cyber threats with higher accuracy. This paper provides a comprehensive review of ML methodologies applied to intrusion detection systems, focusing on their effectiveness, challenges, and future directions.
Despite their advancements, ML based IDS face several challenges, including data imbalance, high computational complexity, and adversarial attacks that manipulate detection mechanisms. The lack of interpretability in deep learning models hinders their deployment in critical security infrastructures. To address these limitations, future research should focus on explainable AI, federated learning for decentralized threat intelligence, and integration with blockchain technology for enhanced data integrity.
 
Keywords: 
Machine Learning (Ml); Intrusion Detection System (Ids); Cybersecurity; Anomaly Detection; Supervised Learning
 
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