Machine Learning Approaches for Intrusion Detection in IoT and Network Security: A Review
DOI:
https://doi.org/10.7494/csci.2026.27.2.7393Abstract
Given the fast-growing number of IoT devices creates advanced cybersecurity headaches for which cutting-edge intrusion detection systems need development to address current and future threats. This paper provides a detailed investigation of ML and DL method applications for IoT and network security, along with a review of their successful implementations and technical constraints.
The review analyzes 30 studies through three different methodological sections which include neural networks, ensemble learning and support vector machines (SVM), deep learning architectures, hybrid AI-blockchain models, and federated learning. Current research shows ensemble learning and deep learning methods as leaders in attack detection since they achieve detection accuracy above 99% for DDoS, phishing and malware attacks. The IoT security is boosted by GA-LSTM and TCN because each model shows high adaptability for changing IoT situations and blockchain integration with AI models strengthens both protection against attacks and data security. The research suffers from four main drawbacks: excessive use of simulated data, scalability problems in IoT devices with limited resources and inadequate field-based verification of systems. The review provides essential guidance for both research scientists and security practitioners who want to develop strong scalable and ethically grounded ML-based security solutions for IoT ecosystems.
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