KerTSDroid: Detecting Android Malware at Scale through Kernel Task Structures The explosive growth of data makes static and dynamic analysis in an individual machine difficult to detect Android malware. Hence many popular opensource frameworks for largescale data processing have been used to deal with iterative machine learning tasks due to the efficient parallel computation and the inmemory abstraction. In this paper we first propose KerTSDroid, a novel Android malware detection framework, to predict the malicious applications in parallel through kernel task structures in Android kernel layer. Then a data provider is orchestrated and implemented to construct the realworld dataset of 112 kernel features from 12,750 Android applications. A parallel predictor is also provided to identify Android malicious applications. Our experiments demonstrate that a Decision Tree method of KerTSDroid can achieve the best precision rate (96%99%) with a lower overhead on average, the other three methods (Naive Bayes, Logistic Regression, and Support Vector Machine) lead to lower precision rate while discriminating abnormal behaviors with the inmemory paralleldata.

FSNet: Android Malware Detection with Only One Feature Traditional Android malware detection based on static analysis are experiencedriven rule writing and feature engineering methods. Such methods only analyze the combination relationship or weight between rules and features of malwares, both of which lack deep representations of the semantics. In this paper, the problem of Android malware detection is considered as task of text categorization, thus we propose FSNet, an effective and efficient datadriven model to solve this problem. FSNet has been designed to learn complex implicit semantic representations from data composed of a singlefeature source. In addition, a new method of feature segmentation is proposed to further improve the performance of the model. Extensive experiments have been conducted. In an evaluation with two large datasets with about 16,000 applications, FSNet shows its remarkable superiority over the traditional methods and outperforms the stateoftheart model at a fraction of the computational cost.