Towards Industrial-Scale Software Binary Analysis

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Bibliografski detalji
Izdano u:PQDT - Global (2025)
Glavni autor: Zhou, Anshunkang
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ProQuest Dissertations & Theses
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100 1 |a Zhou, Anshunkang 
245 1 |a Towards Industrial-Scale Software Binary Analysis 
260 |b ProQuest Dissertations & Theses  |c 2025 
513 |a Dissertation/Thesis 
520 3 |a Computers play an indispensable role in our daily lives, providing a multitude of critical functions and services across areas such as communication, transportation, finance, and beyond. At the heart of modern computing lies a fundamental component: the software binary, a complex sequence of ones and zeroes that determines how specific tasks are executed on a computer. Ensuring the security and correctness of software binaries is of paramount importance, as vulnerabilities can have far-reaching consequences, potentially affecting financial systems and even human lives. However, analyzing software binaries at an industrial scale remains a significant challenge, primarily due to the difficulty of meeting three essential design requirements: rigor, non-intrusiveness, and scalability.This dissertation proposes a binary-centric solution to enhance industrial-scale software binary analysis, addressing these three requirements by contributing to several fundamental binary analysis techniques: binary lifting, binary similarity analysis, and fuzzing.First, we introduce a novel binary lifter, which translates software binaries directly into high-quality compiler-level intermediate representations (IRs) compatible with existing static analyzers, thereby enabling rigorous bug detection. Second, we propose a parallel binary lifting technique to address the scalability limitations of traditional lifters, allowing more efficient utilization of multi-core computers and scaling to extremely large binaries. Building on the IR code obtained through binary lifting, our third contribution is a binary similarity analysis technique that identifies third-party code within software binaries, enabling the reuse of existing knowledge and identification of zero-day vulnerabilities. Finally, extending beyond static analysis, our fourth contribution explores dynamic approaches by proposing a program-adaptive parallel fuzzer, which efficiently generates exploitable bugs with very low false-positive rates through runtime execution.Together, these contributions constitute a systematic solution for software binary analysis, capable of seamless integration into modern software development lifecycles to detect and prevent defects at an early stage. Our approaches have demonstrated tangible real-world impact, being deployed in CI/CD pipelines at major organizations to perform daily software quality checks. Using these techniques, we have successfully identified hundreds of high-risk defects in both industrial software products and open-source projects. Furthermore, our advancements in fundamental binary analysis techniques open avenues for exploration and innovation in related areas of research. 
653 |a Software quality 
653 |a Plankton 
653 |a Programming languages 
653 |a Accuracy 
653 |a Software development 
653 |a Software reliability 
653 |a Success 
653 |a C plus plus 
653 |a Open source software 
653 |a Experiments 
653 |a Optimization 
653 |a Dissertations & theses 
653 |a Breakdowns 
653 |a Third party 
653 |a Algorithms 
653 |a Libraries 
653 |a Ablation 
653 |a Efficiency 
653 |a Semantics 
653 |a Biological oceanography 
653 |a Computer science 
653 |a Microbiology 
773 0 |t PQDT - Global  |g (2025) 
786 0 |d ProQuest  |t ProQuest Dissertations & Theses Global 
856 4 1 |3 Citation/Abstract  |u https://www.proquest.com/docview/3288202058/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch 
856 4 0 |3 Full Text - PDF  |u https://www.proquest.com/docview/3288202058/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch 
856 4 0 |3 Full text outside of ProQuest  |u https://doi.org/10.14711/thesis-hdl152695