[
Alharbi, A.R., Hijji, M., & Aljaedi, A. (2021). Enhancing topic clustering for Arabic security news based on k-means and topic modelling. IET Networks, Vol. 10, Issue 6, 278–294. Available at: https://doi.org/10.1049/ntw2.12017.
]Search in Google Scholar
[
Aluru, S.S., Mathew, B., Saha, P., & Mukherjee, A. (2020). Deep Learning Models for Multilingual Hate Speech Detection. arXiv preprint arXiv:2004.06465, available at: https://doi.org/10.48550/arXiv.2004.06465.
]Search in Google Scholar
[
Ayub, M.F., Li, X., Mahmood, K., Shamshad, S., Saleem, M.A., & Omar, M. (2023). Secure Consumer-Centric Demand Response Management in Resilient Smart Grid as Industry 5.0 Application with Blockchain-Based Authentication. IEEE Transactions on Consumer Electronics. Available at: http://dx.doi.org/10.1109/tce.2023.3320974.
]Search in Google Scholar
[
Chakraborty, S., Krishna, R., Ding, Y., & Ray, B. (2021). Deep Learning Based Vulnerability Detection: Are We There Yet? IEEE Transactions on Software Engineering, Vol. 48, Issue 9, 3280–3296. DOI: 10.1109/TSE.2021.3087402.
]Search in Google Scholar
[
Cheng, X., Wang, H., Hua, J., Xu, G., & Sui, Y. (2021). DeepWukong: Statically Detecting Software Vulnerabilities Using Deep Graph Neural Network. ACM Transactions on Software Engineering and Methodology (TOSEM), Vol. 30, Issue 3, 1–33. Available at: https://doi.org/10.1145/3436877.
]Search in Google Scholar
[
Hanif, H., & Maffeis, S. (2022). VulBERTa: Simplified Source Code Pre-Training for Vulnerability Detection. 2022 IEEE International Joint Conference on Neural Networks (IJCNN), 1–8. DOI: 10.1109/IJCNN55064.2022.9892280.
]Search in Google Scholar
[
Kim, S., Woo, S., Lee, H., & Oh, H. (2017). VUDDY: A Scalable Approach for Vulnerable Code Clone Discovery. 2017 IEEE Symposium on Security and Privacy (SP), 595–614. DOI: 10.1109/SP.2017.62.
]Search in Google Scholar
[
Kim, S., Choi, J., Ahmed, M.E., Nepal, S., & Kim, H. (2022). VulDeBERT: A Vulnerability Detection System Using BERT. 2022 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW), 69–74. DOI: https://doi.org/10.1109/ISSREW55968.2022.00042.
]Search in Google Scholar
[
Li, X., Wang, L., Xin, Y., Yang, Y., & Chen, Y. (2020). Automated vulnerability detection in source code using minimum intermediate representation learning. Applied Sciences, Vol. 10, Issue 5, 1692. Available at: https://doi.org/10.3390/app10051692.
]Search in Google Scholar
[
Li, Z., Zou, D., Xu, S., Jin, H., Qi, H., & Hu, J. (2016). VulPecker: an automated vulnerability detection system based on code similarity analysis. Proceedings of the 32nd Annual Conference on Computer Security Applications, 201–213. Available at: https://doi.org/10.1145/2991079.2991102.
]Search in Google Scholar
[
Omar, M. (2022). Machine Learning for Cybersecurity: Innovative Deep Learning Solutions. Springer International Publishing.
]Search in Google Scholar
[
Omar, M. (2023). Backdoor learning for nlp: Recent advances, challenges, and future research directions. arXiv preprint arXiv. DOI:10.48550/arXiv.2302.06801.
]Search in Google Scholar
[
Omar, M. (2023). VulDefend: A Novel Technique based on Pattern-exploiting Training for Detecting Software Vulnerabilities Using Language Models. 2023 IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology (JEEIT), 287–293. DOI: 10.1109/JEEIT58638.2023.10185860.
]Search in Google Scholar
[
Omar, M., Choi, S., Nyang, D.H., & Mohaisen, D. (2022). Robust natural language processing: Recent advances, challenges, and future directions. arXiv preprint arXiv:2201.00768. Available at: https://arxiv.org/pdf/2201.00768.pdf.
]Search in Google Scholar
[
Omar, M., Jones, R., Burrell, D.N., Dawson, M., Nobles, C., Mohammed, D., & Bashir, A.K. (2023). Harnessing the Power and Simplicity of Decision Trees to Detect IoT Malware. Transformational Interventions for Business, Technology, and Healthcare, 215–229. IGI Global.
]Search in Google Scholar
[
Omar, M., & Sukthankar, G. (2023). Text-defend: Detecting adversarial examples using local outlier factor. 2023 IEEE 17th International Conference on Semantic Computing (ICSC), 118–122. DOI: 10.1109/ICSC56153.2023.00026.
]Search in Google Scholar
[
Rabheru, R., Hanif, H., & Maffeis, S. (2021). DeepTective: Detection of PHP vulnerabilities using hybrid graph neural networks. Proceedings of the 36th Annual ACM Symposium on Applied Computing, 1687–1690. Available at: https://doi.org/10.1145/3412841.3442132.
]Search in Google Scholar
[
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., & Sutskever, I. (2019). Language models are unsupervised multitask learners. OpenAI blog, No. 8, Vol. 1. Available at: https://d4mucfpksywv.cloudfront.net/better-language-models/language-models.pdf.
]Search in Google Scholar
[
Russell, R., Kim, L., Hamilton, L., Lazovich, T., Harer, J., Ozdemir, O., Ellingwood, P., & McConley, M. (2018). Automated vulnerability detection in source code using deep representation learning. 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), 757–762. DOI: 10.1109/ICMLA.2018.00120.
]Search in Google Scholar
[
Salimi, S., & Kharrazi, M. ( 2022). VulSlicer: Vulnerability detection through code slicing. Journal of Systems and Software, Vol. 193, 111450. Available at: https://doi.org/10.1016/j.jss.2022.111450.
]Search in Google Scholar
[
Yamaguchi, F., Golde, N., Arp, D., & Rieck, K. (2014). Modeling and Discovering Vulnerabilities with Code Property Graphs. 2014 IEEE Symposium on Security and Privacy, 590–604. DOI: 10.1109/SP.2014.44.
]Search in Google Scholar
[
Yan, R., Xiao, X., Hu, G., Peng, S., & Jiang, Y. (2018). New deep learning method to detect code injection attacks on hybrid applications. Journal of Systems and Software, Vol. 137, 67–77. DOI:10.1016/j.jss.2017.11.001.
]Search in Google Scholar
[
Zhou, X., & Verma, R.M. (2022). Vulnerability Detection via Multimodal Learning: Datasets and Analysis. Proceedings of the 2022 ACM on Asia Conference on Computer and Communications Security, 1225–1227, 2022. Available at: https://doi.org/10.1145/3488932.3527288.
]Search in Google Scholar
[
Zhou, Y., Liu, S., Siow, J., Du, X., & Liu, Y. (2019). Devign: Effective vulnerability identification by learning comprehensive program semantics via graph neural networks. NIPS Proceedings − Advances in Neural Information Processing Systems, 32, available at: https://papers.nips.cc/book/advances-in-neural-information-processing-systems-32-2019.
]Search in Google Scholar
[
Zou, D., Wang, S., Xu, S., Li, Z., & Jin, H. (2019). μVulDeePecker: A deep learning-based system for multiclass vulnerability detection. IEEE Transactions on Dependable and Secure Computing, Vol. 18, Issue 5, 2224–2236. Available at: https://doi.org/10.1109/TDSC.2019.2942930.
]Search in Google Scholar
