Jump to content
Tuts 4 You

Differentiating Code from Data in x86 Binaries


About This File

Robust, static disassembly is an important part of achieving high coverage for many binary code analyses, such as reverse engineering, malware analysis, reference monitor in-lining, and software fault isolation. However, one of the major diffculties current disassemblers face is differentiating code from data when they are interleaved. This paper presents a machine learning-based disassembly algorithm that segments an x86 binary into subsequences of bytes and then classiffes each subsequence as code or data. The algorithm builds a language model from a set of pre-tagged binaries using a statistical data compression technique. It sequentially scans a new binary executable and sets a breaking point at each potential code-to-code and code-to-data/data-to-code transition. The classiffcation of each segment as code or data is based on the minimum cross-entropy. Experimental results are presented to demonstrate the effectiveness of the algorithm.


User Feedback

Recommended Comments

There are no comments to display.

Create an account or sign in to comment

You need to be a member in order to leave a comment

Create an account

Sign up for a new account in our community. It's easy!

Register a new account

Sign in

Already have an account? Sign in here.

Sign In Now
×
×
  • Create New...