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@inproceedings{Rabheru:2021:10.1145/3412841.3442132,
author = {Rabheru, R and Hanif, H and Maffeis, S},
doi = {10.1145/3412841.3442132},
pages = {1687--1690},
title = {DeepTective: Detection of PHP vulnerabilities using hybrid graph neural networks},
url = {http://dx.doi.org/10.1145/3412841.3442132},
year = {2021}
}

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TY  - CPAPER
AB  - This paper presents DeepTective, a deep learning-based approach to detect vulnerabilities in PHP source code. DeepTective implements a novel hybrid technique that combines Gated Recurrent Units and Graph Convolutional Networks to detect SQLi, XSS and OSCI vulnerabilities leveraging both syntactic and semantic information. Experimental results show that our model outperformed related solutions on both synthetic and realistic datasets, and was able to discover 4 novel vulnerabilities in established WordPress plugins.
AU  - Rabheru,R
AU  - Hanif,H
AU  - Maffeis,S
DO  - 10.1145/3412841.3442132
EP  - 1690
PY  - 2021///
SP  - 1687
TI  - DeepTective: Detection of PHP vulnerabilities using hybrid graph neural networks
UR  - http://dx.doi.org/10.1145/3412841.3442132
ER  -

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