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Differentiating Code from Data in x86 Binaries

Differentiating Code from Data in x86 Binaries

This video was recorded at European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), Athens 2011. 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 isola- tion. However, one of the major diculties current disassemblers face is dierentiating 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 classies each subse- quence 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 classication of each segment as code or data is based on the min- imum cross-entropy. Experimental results are presented to demonstrate the eectiveness of the algorithm.

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