Other approaches to function detection (e.g. At first glance the results of these learners look promising but they are often biased to their training set. Byteweight and Neural Networks) which use signature based pattern recognition. Among some of the approaches enlisted to try and solve this problem are machine learning algorithms (e.g. These foundational analysis artifacts are often the starting point for many automated analysis tools.īeing undecidable, there is no single algorithm to identify all function starts across the wide variety of program binaries. Performing this task accurately is critical. Accurate detection of both function starts and the low-level basic blocks is often the first step in program analysis. and you’re left with an interesting, multifaceted, hard problem. Add to that the numerous types of CPU architectures, compilers, programming languages, application binary interfaces (ABIs), etc. ![]() ![]() Many problems in the program analysis domain fall into this category. Function start detection in stripped binaries is an undecidable problem.
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