Harnessing Broadcast Receivers for Classification of Android Malware Threats





Malware Classification, Android Malware Threats, Broadcast receivers, Malware APKs


With the increasing number of malicious attacks, the way how to detect and classify malicious apps has drawn attention in mobile technology market. In this paper, we proposed a classification model to seek and track malware Apps broadcast receivers in such devices. To identify the family of apps, static features of each app was extracted and a novel deterministic classifier is employed to categorize malware apps. With such, we can act against malware of known family, since we understand its functions, and prevent it from spreading out in larger scale, affecting extensively our society. Detailed description of the classification model is provided, as well the core technologies of this novel malicious android applications’ model are presented. From experiments performed on a set of Android-based malware apps, we observe that the proposed classification model achieves highest accuracy, true-positive rate, false-positive rate, precision, recall, f-measure in comparison to other methods implemented in published experiments. The proposed classification model is promising since the average accuracy reaches an average of 97.31% and can effectively be applied to Android malware categorization, providing early detection of the capabilities of malware and the prospect of warning users of threatens ahead. 

Author Biographies

Panagiotis Karampelas, Hellenic Air Force Academy, Dekeleia, Greece

Dr. Panagiotis Karampelas is an assistant professor at the Hellenic Air Force Academy, Greece. He received his PhD in electronic engineering from University of Kent in 2005. He participates in the advisory board of several scientific journals and conferences. His main research areas are Software Engineering, Data Mining, Cyber Security, Digital Forensics, Social Network Analysis

Konstantinos Xylogiannopoulos, University of Calgary, Calgary, Canada

Konstantinos Xylogiannopoulos is a computer scientist, specialized in pattern detection and big data analytics. He has several publications in applications of pattern detection and real-world problem solutions, such as in bioinformatics, string and text mining, natural language processing, recommendation systems, time series analysis and anomaly detection, network data analytics, etc.