Revamping Binary Analysis with Sampling and Probabilistic Inference
Abstract
Binary analysis, a cornerstone technique in cybersecurity, enables the examination of binary executables, irrespective of source code availability. It plays a critical role in understanding program behaviors, detecting software bugs, and mitigating potential vulnerabilities, specially in situations where the source code remains out of reach. However, aligning the efficacy of binary analysis with that of source-level analysis remains a significant challenge, primarily due to the uncertainty caused by the loss of semantic information during the compilation process.This dissertation presents an innovative probabilistic approach, termed as probabilistic binary analysis, designed to combat the intrinsic uncertainty in binary analysis. It builds on the fundamental principles of program sampling and probabilistic inference, enhanced further by an iterative refinement architecture. The dissertation suggests that a thorough and practical method of sampling program behaviors can yield a substantial quantity of hints which could be instrumental in recovering lost information, despite the potential inclusion of some inaccuracies. Consequently, a probabilistic inference technique is applied to systematically incorporate and process the collected hints, suppressing the incorrect ones, thereby enabling the interpretation of high-level semantics. Furthermore, an iterative refinement mechanism is deployed to augment the efficiency of the probabilistic analysis in subsequent applications, facilitating the progressive enhancement of analysis outcomes through an automated or human-guided feedback loop.This work offers an in-depth understanding of the challenges and solutions related to assessing low-level program representations and systematically handling the inherent uncertainty in binary analysis. It aims to contribute to the field by advancing the development of precise, reliable, and interpretable binary analysis solutions, thereby setting the groundwork for future exploration in this domain.
Degree
Ph.D.
Advisors
Zhang, Purdue University.
Subject Area
Computer science
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