| Title | Deep Learning Toward Preventing Web Attacks |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Hussainy, Abdelrahman S., Khalifa, Mahmoud A., Elsayed, Abdallah, Hussien, Amr, Razek, Mohammed Abdel |
| Conference Name | 2022 5th International Conference on Computing and Informatics (ICCI) |
| Keywords | Computer science, Data models, Deep Learning, Human Behavior, LSTM, Metrics, passwords, policy-based governance, privacy, pubcrawl, resilience, Resiliency, RNN, SQL Injection, SQL injection detection, Training, Web servers, XSS attack |
| Abstract | Cyberattacks are one of the most pressing issues of our time. The impact of cyberthreats can damage various sectors such as business, health care, and governments, so one of the best solutions to deal with these cyberattacks and reduce cybersecurity threats is using Deep Learning. In this paper, we have created an in-depth study model to detect SQL Injection Attacks and Cross-Site Script attacks. We focused on XSS on the Stored-XSS attack type because SQL and Stored-XSS have similar site management methods. The advantage of combining deep learning with cybersecurity in our system is to detect and prevent short-term attacks without human interaction, so our system can reduce and prevent web attacks. This post-training model achieved a more accurate result more than 99% after maintaining the learning level, and 99% of our test data is determined by this model if this input is normal or dangerous. |
| DOI | 10.1109/ICCI54321.2022.9756057 |
| Citation Key | hussainy_deep_2022 |
Deep Learning Toward Preventing Web Attacks