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 the 5th ACNS Workshop on Security in Machine Learning and its Applications (SiMLA 2023) (pp. 642鈥�656). |
|---|---|
| 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, 鈥淔orensic Identification of Android Trojans Using Stacked Ensemble of Deep Neural Networks,鈥� in Proc. 5th ACNS Workshop on Security in Machine Learning and its Applications (SiMLA 2023), Kyoto, Japan, June 19-22, 2023, pp. 642鈥�656. |