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영문 논문지

홈 홈 > 연구문헌 > 영문 논문지 > TIIS (한국인터넷정보학회)

TIIS (한국인터넷정보학회)

Current Result Document : 37 / 38

한글제목(Korean Title) MALICIOUS URL RECOGNITION AND DETECTION USING ATTENTION-BASED CNN-LSTM
영문제목(English Title) MALICIOUS URL RECOGNITION AND DETECTION USING ATTENTION-BASED CNN-LSTM
저자(Author) Yongfang Peng   Shengwei Tian   Long Yu   Yalong Lv   Ruijin Wang  
원문수록처(Citation) VOL 13 NO. 11 PP. 5580 ~ 5593 (2019. 11)
한글내용
(Korean Abstract)
영문내용
(English Abstract)
A malicious Uniform Resource Locator (URL) recognition and detection method based on the combination of Attention mechanism with Convolutional Neural Network and Long Short-Term Memory Network (Attention-Based CNN-LSTM), is proposed. Firstly, the WHOIS check method is used to extract and filter features, including the URL texture information, the URL string statistical information of attributes and the WHOIS information, and the features are subsequently encoded and pre-processed followed by inputting them to the constructed Convolutional Neural Network (CNN) convolution layer to extract local features. Secondly, in accordance with the weights from the Attention mechanism, the generated local features are input into the Long-Short Term Memory (LSTM) model, and subsequently pooled to calculate the global features of the URLs. Finally, the URLs are detected and classified by the SoftMax function using global features. The results demonstrate that compared with the existing methods, the Attention-based CNN-LSTM mechanism has higher accuracy for malicious URL detection.
키워드(Keyword) Malicious URL   Recognition and Detection   Attention-Based CNN-LSTM   Deep Learning  
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