2017 · 25 citations · 31 references
ComputingCybersecurityInformation TechnologyMobile ComputingMobile SecurityMobile SystemsBehavioral AnalysisIntrusion DetectionMobile Application Reverse EngineeringMobile SensingNetwork Traffic AnalysisSuspicious Activity DetectionAndroid Application AnalysisApplication Traffic IdentificationBehavioral Traffic AnalysisExplorationMalicious App DetectionMalicious Behavior AnalysisMalicious Traffic DetectionMobile Malware Detection
Monitoring network behaviors of mobile applications, controlling their resource access and detecting potentially harmful apps are becoming increasingly important for the security protection within today's organizational, ISP and carriers. For this purpose, apps need to be identified from their communication, based upon their individual traffic signatures (called imprints in our research). Creating imprints for a large number of apps is nontrivial, due to the challenges in comprehensively analyzing their network activities at a large scale, for millions of apps on today's rapidly-growing app marketplaces. Prior research relies on automatic exploration of an app's user interfaces (UIs) to trigger its network activities, which is less likely to scale given the cost of the operation (at least 5 minutes per app) and its effectiveness (limited coverage of an app's behaviors).
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