4 results found
Hanif H, Maffeis S, 2022, VulBERTa: Simplified Source Code Pre-Training for Vulnerability Detection, IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) / IEEE World Congress on Computational Intelligence (IEEE WCCI) / International Joint Conference on Neural Networks (IJCNN) / IEEE Congress on Evolutionary Computation (IEEE CEC), Publisher: IEEE, ISSN: 2161-4393
Rabheru R, Hanif H, Maffeis S, 2022, A Hybrid Graph Neural Network Approach for Detecting PHP Vulnerabilities, 5th IEEE Conference on Dependable and Secure Computing (IEEE DSC), Publisher: IEEE
Hanif H, Md Nasir MHN, Ab Razak MF, et al., 2021, The rise of software vulnerability: Taxonomy of software vulnerabilities detection and machine learning approaches, Journal of Network and Computer Applications, Vol: 179, Pages: 103009-103009, ISSN: 1084-8045
Rabheru R, Hanif H, Maffeis S, 2021, DeepTective: Detection of PHP vulnerabilities using hybrid graph neural networks, Pages: 1687-1690
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.
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