• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

MALICIOUS URL RECOGNITION AND DETECTION USING ATTENTION-BASED CNN-LSTM

Vol. 13, No. 11, November 30, 2019
10.3837/tiis.2019.11.017, Download Paper (Free):

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.


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Cite this article

[IEEE Style]
Y. Peng, S. Tian, L. Yu, Y. Lv, R. Wang, "MALICIOUS URL RECOGNITION AND DETECTION USING ATTENTION-BASED CNN-LSTM," KSII Transactions on Internet and Information Systems, vol. 13, no. 11, pp. 5580-5593, 2019. DOI: 10.3837/tiis.2019.11.017.

[ACM Style]
Yongfang Peng, Shengwei Tian, Long Yu, Yalong Lv, and Ruijin Wang. 2019. MALICIOUS URL RECOGNITION AND DETECTION USING ATTENTION-BASED CNN-LSTM. KSII Transactions on Internet and Information Systems, 13, 11, (2019), 5580-5593. DOI: 10.3837/tiis.2019.11.017.

[BibTeX Style]
@article{tiis:22298, title="MALICIOUS URL RECOGNITION AND DETECTION USING ATTENTION-BASED CNN-LSTM", author="Yongfang Peng and Shengwei Tian and Long Yu and Yalong Lv and Ruijin Wang and ", journal="KSII Transactions on Internet and Information Systems", DOI={10.3837/tiis.2019.11.017}, volume={13}, number={11}, year="2019", month={November}, pages={5580-5593}}