Machine Learning approaches in IDS

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.