Detection Approach of Malicious JavaScript Code Based on deep learning

Traditional machine learning methods for detecting JavaScript malicious code have the problems of complex feature extraction process, extensive computation, and difficult detection due to malicious code confusion, which are not conducive to the current requirements of JavaScript malicious code detec...

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Vydané v:2023 IEEE 3rd International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA) Ročník 3; s. 1075 - 1079
Hlavní autori: Zheng, Liyuan, Zhang, Dongcheng, Xie, Xin, Wang, Chen, Hou, Boyuan
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 26.05.2023
Predmet:
Attentional Mechanisms
Bidirectional control
Codes
Deep learning
Learning (artificial intelligence)
Machine learning algorithms
Malicious JavaScript code detection
Neural networks
Production
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Shrnutí:Traditional machine learning methods for detecting JavaScript malicious code have the problems of complex feature extraction process, extensive computation, and difficult detection due to malicious code confusion, which are not conducive to the current requirements of JavaScript malicious code detection accuracy and real-time. This paper proposes a bi-directional long and short-time neural network (BiLSTM) based on an attention mechanism for JavaScript malicious code detection. Firstly, the obtained sample data will be deconfused, disambiguated, and vectorized to obtain the normalized data adapted to the neural network input. Second, the proposed algorithm is used to train the vectorized data and learn the abstract features of the JavaScript malicious code. Finally, the learned features are used to classify the code. The method is compared with deep learning methods and mainstream machine learning methods, and the results show that the method has a high accuracy and low false alarm rate.
DOI:10.1109/ICIBA56860.2023.10165039