Hybridized Machine Learning based IDS for Anomaly Detection: A Systematic Review

Abstract

Intrusion Detection Systems play a crucial role in safeguarding networks against increasingly sophisticated cyber threats. Traditional Intrusion Detection Systems approaches often struggle with adaptability and high false-positive rates. This review investigates the use of hybridized Machine Learning models for anomaly detection in IDS to enhance detection accuracy and system robustness. This study applies the PRISMA framework to analyze hybrid machine learning techniques applied to improve the performance of Intrusion Detection Systems, the datasets used, performance evaluation, identification of challenges, and knowledge gap analysis. Results show that hybrid ML models consistently outperform single-model approaches, achieving an accuracy of up to 99.99%. Despite promising results, challenges such as class imbalance and limited real-time deployment persist. From this systematic review, it is evident that hybridizing machine learning algorithms in Intrusion Detection Systems offers a powerful approach to anomaly detection, improving precision and accuracy.