Software Vulnerability Analysis and Discovery Using Machine-Learning and Data-Mining Techniques

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A Survey of Android Malware Detection with Deep Neural Models (2020, background): “Extracting the robust and informative features is significant to improve the model learning capacity [130].”
<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): “, tens number of lines of code), which is also common to machine learningbased vulnerability detection systems [17].”
SySeVR: A Framework for Using Deep Learning to Detect Software Vulnerabilities (2018, methodology): “Machine learning-based methods, as discussed elsewhere [43], can be further divided into the following three sub-categories.”
Software Vulnerability Detection Using Deep Neural Networks: A Survey (2020, methodology): “An extensive survey [33] has reviewed different approaches that applied conventional ML techniques (nonneural network-based) for vulnerability analysis and discovery.”