ABSTRACT The attacker injects malicious JavaScript into web pages to achieve the purpose of implanting Trojan horses, spreading viruses, phishing, and obtaining secret information. By analyzing the existing researches on malicious JavaScript detection, a malicious JavaScript detection model based on LSTM (Long Short-Term Memory) is proposed. Features are extracted from the semantic level of bytecode, and the method of word vector is optimized. It can distinguish malicious JavaScript code and combat obfuscated code effectively. Experiments showed that the accuracy of detection model based on LSTM is 99.51%, and the F1-score is 98.37%, which is better than the existing model based on Random Forest and SVM algorithm. INDEX TERMS JavaScript, malicious code detection, bytecode, word vector, LSTM.
Y. Fange et al. used Long Short-Term Memory (LSTM) to develop a malicious JavaScript detection model REF .
Research on Malicious JavaScript Detection Technology Based on LSTM
IEEE Access
IEEE Access
Computer Science