A review of Machine learning-based Trojan detection techniques for securing IoT edge devices
Hamza Safdar, Indra Seher, Emadeldin Elgamal, P. W. C. Prasad · 2024
With the rapid advancements on the Internet of Things (IoT) and its applications, the need for securing IoT devices against network intrusions has become increasingly important. Among the primary threats to these devices are denial of Service (DoS) attacks. One effective method for detection of intrusions and DoS attacks is the application of machine learning. Machine learning enables cybersecurity systems to analyze patterns, allowing organizations to allocate their resources more effectively and secure their networks. This study reviews machine learning-based techniques for detecting Trojans to enhance the security of IoT edge devices. Our study focuses on reviewing machine learning-based Trojan detection methods for IoT device security. Our approach involves conducting a literature review of relevant articles and collecting data from these articles. The gathered data is analyzed for classification and verification purposes.