TY  - JOUR
AU  - Alsattam, Faisal 
AU  - Al-Akhras, Mousa 
AU  - Almasri, Marwah M. 
AU  - Alawairdhi, Mohammed 
PY  - 2020
TI  - Rule-Based Approach to Detect IoT Malicious Files
JF  - Journal of Computer Science
VL  - 16
IS  - 9
DO  - 10.3844/jcssp.2020.1203.1211
UR  - https://thescipub.com/abstract/jcssp.2020.1203.1211
AB  - The current immersive increase of cyber-attacks requires constant evolution of the used security solutions. Current malware detection solutions are only able to identify known malwares that were previously detected. They also lack the ability to deeply investigate every file in the system. Therefore, new detection techniques are needed to fill this gab. In this study, a flexible and an effective rule-based approach is proposed to detect malicious files by searching for specific types of strings that should not exist in normal legitimate files. The proposed detection technique relies on the use of LOKI as a scanning agent that uses customized YARA rules with different complexities to search for the needed strings. The proposed methodology has been tested and it detected all malwares successfully.