An Improved Tool for Detection of XSS Attacks by Combining CNN with LSTM


Cross-Site Scripting (XSS) is still a significant threat to web applications. By combining Convolutional Neural Networks (CNN) with Long ShortTerm Memory (LSTM) techniques, researchers have developed a deep learning system called 3C-LSTM that achieves upwards of 99.4% accuracy when predicting whether a new URL corresponds to a benign locator or an XSS attack. This paper improves on 3C-LSTM by proposing different network architectures and validation strategies and identifying the optimal structure for a more efficient, yet similarly accurate, version of 3C-LSTM. The authors identify larger batch sizes, smaller inputs, and cross-validation removal as modifications to achieve a speedup of around 3.9 times in the training step.

Palavras-chave: cross-site scripting, communication system security, machine learning, natural language processing


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LENTE, Caio; HIRATA JR., Roberto; BATISTA, Daniel Macêdo. An Improved Tool for Detection of XSS Attacks by Combining CNN with LSTM. In: SALÃO DE FERRAMENTAS - SIMPÓSIO BRASILEIRO DE SEGURANÇA DA INFORMAÇÃO E DE SISTEMAS COMPUTACIONAIS (SBSEG), 21. , 2021, Evento Online. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2021 . p. 1-8. DOI:

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