The International Arab Journal of Information Technology (IAJIT)


Enhanced Android Malware Detection and Family Classification, using Conversation-level Network

Signature-based malware detection algorithms are facing challenges to cope with the massive number of threats in the Android environment. In this paper, conversation-level network traffic features are extracted and used in a supervised- based model. This model was used to enhance the process of Android malware detection, categorization, and family classification. The model employs the ensemble learning technique in order to select the most useful features among the extracted features. A real-world dataset called CICAndMal2017 was used in this paper. The results show that Extra-trees classifier had achieved the highest weighted accuracy percentage among the other classifiers by 87.75%, 79.97%, and 66.71%for malware detection, malware categorization, and malware family classification respectively. A comparison with another study that uses the same dataset was made. This study has achieved a significant enhancement in malware family classification and malware categorization. For malware family classification, the enhancement was 39.71% for precision and 41.09% for recall. The rate of enhancement for the Android malware categorization was 30.2% and 31.14% for precision and recall, respectively.

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[38] Zulkifli A., Hamid I., Shah W., and Abdullah Z., “Android Malware Detection Based on Network Traffic Using Decision Tree Algorithm,” International Conference on Soft Computing and Data Mining, Johor, pp. 485-494, 2018. Mohammad Abuthawabeh received his master's degree from Princess Sumaya University for Technology, Jordan, in 2019. Currently, he is an information systems specialist of Jordan Anti Money Laundering and Counter Terrorism Unit, and a freelance information security researcher. His research interest includes Information security and machine learning. Khaled Mahmoud get his BSc degree in Computer Science from Jordan University on June 1992, MSc degree in Computer Science (Artificial Intelligence) from Jordan University on 1998 and PhD degree in Print Security and Digital Watermarking from Loughborough University (UK) on 2004. This was followed by academic appointments at ZARQA Private University as an assistance Professor in computer Science. On 2018 he joined Princess Sumaya University as an academic staff in computer science department. His areas of interest include Information security, Digital watermarking, Image forgery detection, AI and Arabic language processing.