Lightweight Classifier for Obfuscation Methods for IoT Devices
Wanju Kim, Youjeong Noh, Seoksu Lee, Eun-Sun Cho · 2025
Identifying obfuscation techniques is essential for effective malware analysis and mitigation. Previous research has primarily focused on x86 architectures, but obfuscation techniques have become increasingly prevalent in IoT malware, particularly on ARM and MIPS architectures.This paper presents a method for identifying obfuscation techniques across different architectures. We first analyze and enhance an existing approach for x86 malware, improving its accuracy and efficiency. We then extend the method to IoT architectures, evaluating how architectural differences influence obfuscation patterns. Experimental results confirm that the proposed method effectively detects obfuscation techniques across multiple platforms, providing a foundation for more robust malware analysis in IoT environments. This research strengthens security measures by enabling more effective deobfuscation strategies against emerging threats.