Machine Learning Approaches for Intrusion Detection in Modern Networks
DOI:
https://doi.org/10.15662/IJARCST.2023.0605002Keywords:
Machine Learning, Intrusion Detection Systems, Network Security, Supervised Learning, Unsupervised Learning, Deep Learning, Feature Selection, Anomaly Detection, Cyber ThreatsMDPI+1MDPI+6SpringerOpen+6arXiv+6MDPI+8arXiv+8arXiv+8Abstract
Machine learning (ML) has emerged as a pivotal tool in enhancing the efficacy of intrusion detection systems (IDS) within modern networks. Traditional signature-based IDS methods often falter against novel or sophisticated attacks due to their reliance on predefined patterns. In contrast, ML-based IDS can autonomously learn from data, identifying complex attack patterns and adapting to evolving threats.
This paper provides a comprehensive review of ML techniques employed in IDS, focusing on their application in contemporary network environments. We examine various ML algorithms, including supervised, unsupervised, and deep learning models, highlighting their strengths and limitations in detecting a wide array of network intrusions. Additionally, we explore the challenges associated with implementing ML in IDS, such as data imbalance, feature selection, and model interpretability.
Through an analysis of recent studies and datasets, we assess the performance of different ML approaches in real-world scenarios. The findings underscore the importance of selecting appropriate algorithms and preprocessing techniques to optimize detection accuracy and minimize false positives. Furthermore, we discuss the integration of ML-based IDS with existing network security infrastructures and the potential for real-time threat detection.
In conclusion, while ML offers significant advancements in IDS, ongoing research is essential to address existing challenges and enhance the robustness of these systems against emerging cyber threats.
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