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The amount of Android malware has increased greatly during the last fewyears. Static analysis is widely used in detecting such malware by analyzingthe code without execution. The effectiveness of current tools relies on theapp model as well as the malware detection algorithm which analyzes the appmodel. If the model and/or the algorithm is inadequate, then sophisticatedattacks that are triggered by specific sequences of events will not bedetected.  This paper presents a static analysis framework called Dexteroid, which usesreverse-engineered life cycle models to accurately capture the behaviors ofAndroid components. Dexteroid systematically derives event sequences from themodels, and uses them to detect attacks launched by specific ordering ofevents. A prototype implementation of Dexteroid detects two types of attacks:(1) leakage of private information, and (2) sending SMS to premium-ratenumbers. A series of experiments are conducted on 1526 Google Play apps, 1259Genome Malware apps, and a suite of benchmark apps called DroidBench and theresults are compared with a state-of-the-art static analysis tool calledFlowDroid. The evaluation results show that the proposed framework is effectiveand efficient in terms of precision, recall, and execution time.

Dexteroid: Detecting Malicious Behaviors in Android Apps Using Reverse-Engineered Life Cycle Models