Using Machine Learning Algorithms in Intrusion Detection Systems: A Review - Mohammed, Mazin S.; Talib, Hasanien Ali (Tikrit Journal of Pure Science, 2024)
Intrusion Detection Systems (IDS) are essential for identifying and mitigating security threats in Internet of Things (IoT) networks. This paper explores the unique challenges of IoT environments and presents machine learning (ML) algorithms as powerful solutions for IoT-IDS, encompassing supervised, unsupervised, and semi-supervised learning. Notable algorithms, including decision trees, random forests, support vector machines, and deep learning architectures, are discussed. Emphasis is placed on the critical role of feature selection in developing efficient IDS, addressing challenges such as heterogeneity, limited resources, real-time detection, privacy concerns, and adversarial attacks. Future research directions include advanced ML algorithms for IoT data, integration of anomaly detection, exploration of federated learning, and combining ML with other cybersecurity techniques. The paper advocates for benchmark datasets and evaluation frameworks to standardize the assessment of ML-based IoT-IDS approaches, ultimately contributing to heightened security and integrity in IoT systems..