TIIS (한국인터넷정보학회)
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| 한글제목(Korean Title) |
LSTM Android Malicious Behavior Analysis Based on Feature Weighting |
| 영문제목(English Title) |
LSTM Android Malicious Behavior Analysis Based on Feature Weighting |
| 저자(Author) |
Qing Yang
Xiaoliang Wang
Jing Zheng
Wenqi Ge
Ming Bai
Frank Jiang
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| 원문수록처(Citation) |
VOL 15 NO. 06 PP. 2188 ~ 2203 (2021. 06) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
With the rapid development of mobile Internet, smart phones have been widely popularized, among which Android platform dominates. Due to it is open source, malware on the Android platform is rampant. In order to improve the efficiency of malware detection, this paper proposes deep learning Android malicious detection system based on behavior features. First of all, the detection system adopts the static analysis method to extract different types of behavior features from Android applications, and extract sensitive behavior features through Term frequency-inverse Document Frequency algorithm for each extracted behavior feature to construct detection features through unified abstract expression. Secondly, Long Short-Term Memory neural network model is established to select and learn from the extracted attributes and the learned attributes are used to detect Android malicious applications, Analysis and further optimization of the application behavior parameters, so as to build a deep learning Android malicious detection method based on feature analysis. We use different types of features to evaluate our method and compare it with various machine learning-based methods. Study shows that it outperforms most existing machine learning based approaches and detects 95.31% of the malware. |
| 키워드(Keyword) |
android security
Deep Learning
LSTM
Static Analysis
TF-IDF
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| 파일첨부 |
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