<p>Binary analysis, a cornerstone technique in cybersecurity, enables the examination of binary executables, irrespective of source code availability.</p>
<p>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.</p>
<p>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.</p>
<p><br></p>
<p>This dissertation presents an innovative probabilistic approach, termed as <em>probabilistic binary analysis</em>, designed to combat the intrinsic uncertainty in binary analysis.</p>
<p>It builds on the fundamental principles of program sampling and probabilistic inference, enhanced further by an iterative refinement architecture.</p>
<p>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.</p>
<p>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.</p>
<p>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.</p>
<p><br></p>
<p>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. </p>
<p>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.</p>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/23542014 |
| Date | 19 June 2023 |
| Creators | Zhuo Zhang (16398420) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/thesis/Revamping_Binary_Analysis_with_Sampling_and_Probabilistic_Inference/23542014 |
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