Deep Learning applied on Web and IoT Security
We study if deep learning applications can help in information security on detection of XSS vulnerabilities and other injection attacks related to the web and the web of things.
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In this work, we explore static approaches to detect XSS vulnerabilities using neural networks. We compare two different code representations based on NLP and PLP and generate models using different neural network architectures for static analysis.
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The Concatenation Detector helps you to build deep learning models to detect statically web vulnerability - especially Cross-Site Scripting XSS - based on Natural Language Processing (NLP)
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The Hashed-AST Detector helps you to build deep learning models to detect statically web vulnerability - especially Cross-Site Scripting XSS - based on Programming Language Processing (PLP)