ARCHIVES

1. Halfond, W. G. J., Viegas, J., & Orso, A. (2006). A classification of SQL injection attacks and countermeasures. Proceedings of the
International Symposium on Secure Software Engineering, 13–15.
2. Valeur, F., Vigna, G., Kruegel, C., & Kemmerer, R. A. (2004). A comprehensive approach to intrusion detection alert correlation. IEEE
Transactions on Dependable and Secure Computing, 1(3), 146–169.
3. Shar, L. K., & Tan, H. B. K. (2013). Defeating SQL injection. IEEE Computer, 46(3), 69–77.
4. Anand, P., Ryoo, J., & Moon, J. (2016). Detecting insider threats using behavioural analytics based on hidden Markov models. Proceedings
of the 2016 IEEE International Conference on Big Data, 1250–12595. Kamra, A., Terzi, E., & Bertino, E. (2008). Detecting anomalous access patterns in relational databases. The VLDB Journal, 17(5), 1063–
1077.
6. Mukhopadhyay, I., Chakraborty, M., & Chakrabarti, S. (2011). A comparative study of related technologies of intrusion detection and
prevention systems. Journal of Information Security, 2(1), 28–38.
7. Li, Z., Qin, Z., Huang, K., Yang, X., & Ye, S. (2018). Intrusion detection using convolutional neural networks for representation learning.
Proceedings of the International Conference on Neural Information Processing (ICONIP), 858–866.
8. Fang, Y., Zeng, Y., Li, B., Liu, L., & Zhang, L. (2021). Detecting cyberattacks in smart grids using graph neural networks with temporal
attention. Future Generation Computer Systems, 129, 188–202.
9. Roy, S. S., Mallik, A., Gulati, R., Obaidat, M. S., & Krishna, P. V. (2017). A deep learning based artificial neural network approach for
intrusion detection. Proceedings of International Conference on Mathematics and Computing, 44–53.
10. Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). 'Why should I trust you?': Explaining the predictions of any classifier. Pr oceedings of
the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1135–1144.
11. Warnecke, A., Arp, D., Wressnegger, C., & Rieck, K. (2020). Evaluating explanation methods for deep learning in security. Proceedings
of the 5th IEEE European Symposium on Security and Privacy (EuroS&P), 158–174.
12. Lundberg, S. M., & Lee, S. I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing
Systems (NeurIPS), 30, 4765–4774.
13. Apruzzese, G., Colajanni, M., Ferretti, L., Guido, A., & Marchetti, M. (2018). On the effectiveness of machine and deep learning for
cyber security. Proceedings of the 10th International Conference on Cyber Conflict (CyCon), 371–390.
14. Buczak, A. L., & Guven, E. (2016). A survey of data mining and machine learning methods for cybersecurity intrusion detection. IEEE
Communications Surveys and Tutorials, 18(2), 1153–1176.
15. Dong, Y., & Li, J. (2021). Adversarial attack and defense techniques for deep learning-based network intrusion detection systems. IEEE
Access, 9, 82434–82450.