Toward Machine Learning Based Analyses on Compressed Firmware
Seoksu Lee, Joon‐Young Paik, Rize Jin, Eun-Sun Cho · 2019
As Internet of Things (IoT) applications are getting attention these days, the importance of firmware security is also growing. However, it is not straightforward to analyze the bugs or vulnerabilities that reside in firmware. One of the major challenges is to detect information about hardware architectures of compressed firmware. Traditional analysis tools make use of static signatures embedded in the compressed binary code of firmware. However, signature extraction needs the careful elaboration of experts, and it is not always even possible. In this paper, we introduce our experience in analyzing the hardware information of compressed firmware. Since it is not possible to use the semantic information of compressed binary code, we adopt machine learning technologies for this purpose. Despite various difficulties, we have positive experimental results.