3260 papers • 126 benchmarks • 313 datasets
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These leaderboards are used to track progress in mobile-security-6
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Use these libraries to find mobile-security-6 models and implementations
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This paper focuses on building a hybrid approach of static and dynamic analysis for detecting the vulnerabilities of Android applications, and detects information leaks as well as insecure network requests alongside other commonly detected flaws that harm users’ privacy.
This study proposes a simple but effective approach to hacking deep learning models using adversarial attacks by identifying highly similar pre-trained models from TensorFlow Hub, and investigates the characteristics ofDeep learning models used by hundreds of Android apps on Google Play.
A systematic literature review was conducted to search and analyze how DL approaches have been applied in the context of malware defenses in the Android environment and reveals that, while the majority of these sources mainly consider DL-based Android malware detection, 53 primary studies (40.1%) design defense approaches based on other scenarios.
This paper proposes and develops an intelligent system named Dr.Droid to jointly model malware propagation and evolution for their detection at the first attempt, and proposes a novel heterogeneous temporal graph transformer framework (denoted as HTGT) to integrate both spatial and temporal dependencies while preserving the heterogeneity.
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