L-IDS: A Multi-Layered Approach to Ransomware Detection in IoT
Farhad Mofidi, Sena G Hounsinou, Gedare Bloom · 2024
Ransomware is increasingly targeting Internet of Things (IoT) devices, and the resource constraints of these devices make detecting and mitigating such attacks a significant challenge. Unlike traditional ransomware attacks, ransomware in IoT-based attacks aims to affect functionality rather than the availability of data, thus defeating traditional detection methods. To address this issue, this article introduces a lightweight intrusion detection system, L-IDS. It is designed based on the principle of defense in depth and combines multilayer controls, hardware-enhanced TEE such as TrustZone, with machine learning (ML) algorithms. L-IDS can effectively detect and mitigate ransomware attacks inside IoT systems with low resources compared to traditional security scanning methods. By integrating TEE, L-IDS will enhance the security and protection of IoT devices, while ML algorithms will help detect ransomware attacks more efficiently and accurately. Overall, the proposed approach provides a promising solution for protecting IoT systems against ransomware attacks, especially for resource-constrained devices.