Structural Attack against Graph Based Android Malware Detection
Kaifa Zhao, Hao Zhou, Yulin Zhu, Xian Zhan, Kai Zhou, Jianfeng Li, Le Yu, Wei Yuan, Xiapu Luo
Abstract
Malware detection techniques achieve great success with deeper insight into the semantics of malware. Among existing detection techniques, function call graph (FCG) based methods achieve promising performance due to their prominent representations of malware's functionalities. Meanwhile, recent adversarial attacks not only perturb feature vectors to deceive classifiers (i.e., feature-space attacks) but also investigate how to generate real evasive malware (i.e., problem-space attacks). However, existing problem-space attacks are limited due to their inconsistent transformations between feature space and problem space.
Topics & Concepts
MalwareComputer scienceAndroid malwareFeature vectorAndroid (operating system)CryptovirologyAdversarial systemGraphComputer securityTheoretical computer scienceArtificial intelligenceOperating systemAdvanced Malware Detection TechniquesNetwork Security and Intrusion DetectionSpam and Phishing Detection