Network TrafficBased Hybrid Malware Detection for Smartphone and Traditional Networked Systems With the widespread use of the Internet in recent times, security remains one of the major concerns. Malware poses security threats to smartphones, computers, and networks. These threats require an urgent need to build an efficient hybrid intrusion detection system, which can detect malware from smartphone and traditional systems, and ensure minimal damage to the resources of an organization. In this paper, we propose an intelligent and selflearning network trafficbased hybrid malware detection approach (HMDA) for smartphones and traditional systems considering features that show a similar trend in the network traffic. The system could be used by an organizational network to detect and mitigate any occurrence of malwarebased malicious activity inside the network. The proposed HMDA is implemented using machine learning algorithms. We have used ensemble learners to train the model for the HMDA and achieved an accuracy of 95.7% using XGBoost algorithm. The Android traffic captures collected by running the malware dataset have been made publicly available upon request to authors.

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.