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Forensic Identification of Android Trojans Using Stacked Ensemble of Deep Neural Networks
Summary
As the user base of Android operating systems grows steadily, the ecosystem became a growing target for malicious actors. With Trojans representing over 93% of all Android malware, this type of malicious code becomes a serious threat to Android users. In this paper, we present a forensic identification system to identify Android trojan families based on dynamic features extracted from malicious applications. Our proposed system is based on a stacked ensemble of deep neural networks. The proposed system was tested using CIC-AndMal-2020 dataset, and has shown accuracy and F1 score exceeding 0.98 in identifying trojan families effectively.
Conference: 5th ACNS Workshop on Security in Machine Learning and its Applications (SiMLA 2023)
Location: Kyoto, Japan
Date: June 19-22, 2023
Keywords
android, trojan, malware, classification
Links
References
| APA | Alani, M. M., Mashatan, A. & Miri, A. (2023). Forensic Identification of Android Trojans Using Stacked Ensemble of Deep Neural Networks. Proceedings of 5th ACNS Workshop on Security in Machine Learning and its Applications (SiMLA 2023). |
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| BibTeX | @INPROCEEDINGS{forensic-2023, author={Mohammed M Alani and Atefeh Mashatan and Ali Miri}, booktitle={5th ACNS Workshop on Security in Machine Learning and its Applications (SiMLA 2023)}, title={Forensic Identification of Android Trojans Using Stacked Ensemble of Deep Neural Networks}, year={2023} } |
| IEEE | M. M. Alani, A. Mashatan, and A. Miri, “Forensic Identification of Android Trojans Using Stacked Ensemble of Deep Neural Networks,” in Proc. 5th ACNS Workshop on Security in Machine Learning and its Applications (SiMLA 2023), Kyoto, Japan, June 19-22, 2023, pp. 642–656. |