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Combined Static Analysis and Machine Learning Prediction for Application Debloating
April 2, 2024, 7:11 p.m. | Chris Porter, Sharjeel Khan, Kangqi Ni, Santosh Pande
cs.CR updates on arXiv.org arxiv.org
Abstract: Software debloating can effectively thwart certain code reuse attacks by reducing attack surfaces to break gadget chains. Approaches based on static analysis enable a reduced set of functions reachable at a callsite for execution by leveraging static properties of the callgraph. This achieves low runtime overhead, but the function set is conservatively computed, negatively affecting reduction. In contrast, approaches based on machine learning (ML) have much better precision and can sharply reduce function sets, leading …
analysis application arxiv attack attacks attack surfaces can code code reuse cs.cr effectively enable functions gadget low machine machine learning prediction reuse runtime software static analysis
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