VUDDY: A Scalable Approach for Vulnerable Code Clone Discovery

By Seulbae Kim, Seunghoon Woo, Heejo Lee, Hakjoo Oh. IEEE Symposium on Security and Privacy, 2017

402 citations recorded by the source corpus.

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Software Vulnerability Detection Using Deep Neural Networks: A Survey (2020, methodology): “Based on the network of Bi-GRU, the authors compared the proposed framework with other state-of-the-art vulnerability detection systems, including Flawﬁnder [113], RATS [4], Checkmarx, VUDDY [45], and VulDeePecker [56], and their framework achieved the best detection performance.”
<inline-formula><tex-math notation="LaTeX">$\mu$</tex-math><alternatives><mml:math><mml:mi>μ</mml:mi></mml:math><inline-graphic xlink:href="zou-ieq1-2942930.gif"/></alternatives></inline-formula>VulDeePecker: A Deep Learning-Based System for Multiclass Vulnerability Detection (2019, neutral): “this explains why detecting a piece of code that similar to a piece of vulnerable code could detect vulnerabilities. In this approach, similarity can be measured using tokens [10], [42], strings [7], [43], trees [44], [45], graphs [8], or their hybrids [46]. This approach cannot detect vulnerabiliti”
SySeVR: A Framework for Using Deep Learning to Detect Software Vulnerabilities (2018, methodology): “Source code-based static analysis is an important approach to detecting vulnerabilities, including code similarity-based methods [2], [3] and pattern-based methods [4], [5], [6], [7], [8], [9], [10].”