Deep Learning Approaches for Malware Detection in the Industrial Internet of Things: A Comprehensive Analysis
Ghassan Samara, Mohammad Abu Fadda, Abeer Al-Mohtaseb, Raed Alazaidah, Alrefai Mohamed N., Mahmoud Odeh, Mohammad Aljaidi, Mohammed Mahmod Shuaib, Mohammad A. Kanan · 2024
This survey aims to identify hazards associated with the Internet of Things (IoT), focusing on malware that can infiltrate various devices, applications, and systems within the Industrial Internet of Things (IIoT). Such malware enables malicious actors to manipulate these systems. This research utilizes deep learning as a method for detecting malicious programs. Additionally, it provides hypotheses and background information on deep learning and the IIoT, along with an overview of the types of malware that can compromise IoT applications. Finally, the study presents findings related to the research hypotheses and relevant topics.