Android Malware Detection Using Parallel Machine Learning Classifiers

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Mobile malware has continued to grow at an alarming rate despite on-going mitigation efforts. This has been much more prevalent on Android due to being an open platform that is rapidly overtaking other competing platforms in the mobile smart devices market. Recently, a new generation of Android malware families has emerged with advanced evasion capabilities which make them much more difficult to detect using conventional methods. This paper proposes and investigates a parallel machine learning based classification approach for early detection of Android malware. Using real malware samples and benign applications, a composite classification model is developed from parallel combination of heterogeneous classifiers. The empirical evaluation of the model under different combination schemes demonstrates its efficacy and potential to improve detection accuracy. More importantly, by utilizing several classifiers with diverse characteristics, their strengths can be harnessed not only for enhanced Android malware detection but also quicker white box analysis by means of the more interpretable constituent classifiers

Analysis and evaluation of SafeDroid v2.0, a framework for detecting malicious Android applications

Android smartphones have become a vital component of the daily routine of millions of people, running a plethora of applications available in the official and alternative marketplaces. Although there are many security mechanisms to scan and filter malicious applications, malware is still able to reach the devices of many end-users. In this paper, we introduce the SafeDroid v2.0 framework, that is a flexible, robust, and versatile open-source solution for statically analysing Android applications, based on machine learning techniques. The main goal of our work, besides the automated production of fully sufficient prediction and classification models in terms of maximum accuracy scores and minimum negative errors, is to offer an out-of-the-box framework that can be employed by the Android security researchers to efficiently experiment to find effective solutions: the SafeDroid v2.0 framework makes it possible to test many different combinations of machine learning classifiers, with a high degree of freedom and flexibility in the choice of features to consider, such as dataset balance and dataset selection. The framework also provides a server, for generating experiment reports, and an Android application, for the verification of the produced models in real-life scenarios. An extensive campaign of experiments is also presented to show how it is possible to efficiently find competitive solutions: the results of our experiments confirm that SafeDroid v2.0 can reach very good performances, even with highly unbalanced dataset inputs and always with a very limited overhead

Ensemble Method for Mobile Malware Detection using N-Gram Sequences of System Calls

Mobile device has become an essential tool among the community across the globe and has turned into a necessity in daily life. An extensive usage of mobile devices for everyday life tasks such as online banking, online shopping and exchanging e-mails has enable mobile devices to become data storage for users. The data stored in these mobile devices can contain sensitive and critical information to the users. Hence, making mobile devices as the prime target for cybercriminal. To date, Android based mobile devices is one of the mobile devices that are dominating the phone market. Moreover, the ease of use and open-source feature has made Android based mobile devices popular. However, the widely used Android mobile devices has encourage malware author to write malicious application. In a short duration of time mobile malware has rapidly evolve and have the capability to bypass signature detection approach which requires a constant signature update to detect mobile malware. To overcome this drawback an anomaly detection approach can be used to mitigate this issue. Yet, using a single classifier in an anomaly detection approach will not improve the classification detection performance. Based on this reason, this research formulates an ensemble classification method of different n-gram system call sequence features to improve the accuracy of mobile malware detection. This research proposes n-number of classifier models for each different n-gram sequence call feature. The probability output of each classifier is then combined to produce a better classification performance which is better compared to a single classifier