Exploring Machine Learning Intrusion Detection: Addressing Security and Privacy Challenges in IoT - A Comprehensive Review
Gowrisankar Krishnamoorthy, Sai Mani Krishna Sistla
Abstract
With billions of IoT devices in operation globally, vast amounts of data are generated, posing significant security challenges throughout the data lifecycle. Machine learning (ML) offers a promising approach to safeguarding IoT systems by swiftly detecting anomalies and enforcing real-time security and privacy (S&P) measures. This systematic literature review investigates ML-based intrusion detection in IoT, examining academic journals from 2011 to 2021 through the IEEE and ProQuest databases. Utilizing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, we identify key insights and challenges. Our review reveals that while ML-based Intrusion Detection Systems (IDS) exhibit superior performance in detecting emerging attack trends, they also introduce complexities such as increased computational demands, susceptibility to adversarial attacks, scalability issues, and trade-offs between accuracy and false positives. Furthermore, deep learning methods outperform traditional ML techniques in anomaly detection. Addressing the evolving nature of attacks remains a continuous endeavor, underscoring the ongoing development of IDS.