A Review and Comparative Analysis of Intrusion Detection Systems for Edge Networks in IoT - Amrullah, Ata (Intellithings Journal, 2025)
This article presents a comprehensive review and comparative analysis of intrusion detection systems (IDS) designed for edge networks in Internet of Things (IoT) environments. The rapid growth of IoT has heightened security vulnerabilities in edge networks, which are the focus of this study. Various IDS approaches, including signature-based, anomaly-based, and hybrid methods, are explored, with an emphasis on the application of machine learning and deep learning techniques. The review includes an analysis of system architectures, algorithms (LSTM, CNN, Transformer), datasets, performance evaluation metrics, and experimental results from prior research. The literature study indicates that deep learning has significant potential to enhance intrusion detection accuracy; however, its effectiveness depends on dataset quality, appropriate data preprocessing, and handling class imbalances. Optimal feature selection, blockchain integration, and ensemble approaches are also critical. In conclusion, a multi-faceted approach combining advanced algorithms, suitable preprocessing techniques, and a deep understanding of IoT attacks is essential. Future research should focus on developing adaptive, efficient, and robust IDS with realistic datasets and comprehensive evaluation methods. This article provides a valuable resource for researchers and practitioners in the field of IoT edge network security.