LSTM-Based Hierarchical Denoising Network for Android Malware Detection
Joint Authors
Source
Security and Communication Networks
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-18, 18 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-01-09
Country of Publication
Egypt
No. of Pages
18
Main Subjects
Information Technology and Computer Science
Abstract EN
Mobile security is an important issue on Android platform.
Most malware detection methods based on machine learning models heavily rely on expert knowledge for manual feature engineering, which are still difficult to fully describe malwares.
In this paper, we present LSTM-based hierarchical denoise network (HDN), a novel static Android malware detection method which uses LSTM to directly learn from the raw opcode sequences extracted from decompiled Android files.
However, most opcode sequences are too long for LSTM to train due to the gradient vanishing problem.
Hence, HDN uses a hierarchical structure, whose first-level LSTM parallelly computes on opcode subsequences (we called them method blocks) to learn the dense representations; then the second-level LSTM can learn and detect malware through method block sequences.
Considering that malicious behavior only appears in partial sequence segments, HDN uses method block denoise module (MBDM) for data denoising by adaptive gradient scaling strategy based on loss cache.
We evaluate and compare HDN with the latest mainstream researches on three datasets.
The results show that HDN outperforms these Android malware detection methods,and it is able to capture longer sequence features and has better detection efficiency than N-gram-based malware detection which is similar to our method.
American Psychological Association (APA)
Yan, Jinpei& Qi, Yong& Rao, Qifan. 2018. LSTM-Based Hierarchical Denoising Network for Android Malware Detection. Security and Communication Networks،Vol. 2018, no. 2018, pp.1-18.
https://search.emarefa.net/detail/BIM-1214203
Modern Language Association (MLA)
Yan, Jinpei…[et al.]. LSTM-Based Hierarchical Denoising Network for Android Malware Detection. Security and Communication Networks No. 2018 (2018), pp.1-18.
https://search.emarefa.net/detail/BIM-1214203
American Medical Association (AMA)
Yan, Jinpei& Qi, Yong& Rao, Qifan. LSTM-Based Hierarchical Denoising Network for Android Malware Detection. Security and Communication Networks. 2018. Vol. 2018, no. 2018, pp.1-18.
https://search.emarefa.net/detail/BIM-1214203
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1214203