@inproceedings{f2656758f07d40faa6f1d8d874b7f199,
title = "DeepDetector: Android Malware Detection using Deep Neural Network",
abstract = "The security issue of Android Smart Phones has been most concerned by the users these years. We propose a novel method to extract exhaustive features from Android applications and use the deep-learning-based method to detect malicious applications. Then we implement an automatic detection engine, DeepDetector, to detect malicious applications. Furthermore, the detection model can identify the fine-grained malware family at the same time. We conducted the evaluation and analysis with thousands of malicious applications and benign applications from a public dataset. The results show that DeepDetector can detect 97\% of the malware at 0.1 \% false positive rate (FPR) and achieves a 96\% precision when showing the detailed malware families. Besides, the relationship between the detection performance and the architecture of the neural network is also discussed in our work.",
keywords = "Android Malware Detection, Deep Neural Network, Fine-grained Classification, Smartphones Security",
author = "Dongfang Li and Zhaoguo Wang and Yibo Xue",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 International Conference on Advances in Computing and Communication Engineering, ICACCE 2018 ; Conference date: 22-06-2018 Through 23-06-2018",
year = "2018",
month = aug,
day = "20",
doi = "10.1109/ICACCE.2018.8441737",
language = "英语",
isbn = "9781538644850",
series = "Proceedings on 2018 International Conference on Advances in Computing and Communication Engineering, ICACCE 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "184--188",
editor = "Vishal Kumar and Sudarsan, \{S. D.\} and Ravi Tomar",
booktitle = "Proceedings on 2018 International Conference on Advances in Computing and Communication Engineering, ICACCE 2018",
address = "美国",
}