Distinguishing Look-Alike Innocent and Vulnerable Code by Subtle Semantic Representation Learning and Explanation
Chao Ni, Xin Yin, Kaiwen Yang, Dehai Zhao, Zhenchang Xing, Xin Xia
Abstract
Though many deep learning (DL)-based vulnerability detection approaches have been proposed and indeed achieved remarkable performance, they still have limitations in the generalization as well as the practical usage. More precisely, existing DL-based approaches (1) perform negatively on prediction tasks among functions that are lexically similar but have contrary semantics; (2) provide no intuitive developer-oriented explanations to the detected results.
Topics & Concepts
Computer scienceGeneralizationSemantics (computer science)Code (set theory)Representation (politics)Artificial intelligenceNatural language processingDeep learningMachine learningProgramming languageMathematicsPolitical scienceSet (abstract data type)PoliticsMathematical analysisLawAdversarial Robustness in Machine LearningAdvanced Malware Detection TechniquesSoftware Engineering Research