Abstract
The rise in usage of the Internet has tremendously helped those who use web applications. Web-based applications are becoming more susceptible to numerous security risks and network vulnerabilities as online attacks continue to develop. Malicious code or contents could be embedded in requests from HTTP causing attacks like SQL injections etc.In this research, an online intrusion detection system is presented to tackle the rise in web application attacks. Our web intrusion detection system uses a Distil-BERT, RNN, and LSTM model to identify attacks with body, URL, and User-data. The experimental findings demonstrate that our model successfully classifies the attacks with body, URL, and user data with a 94% accuracy.
Original language | English |
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Title of host publication | ISDFS 2023 - 11th International Symposium on Digital Forensics and Security |
Editors | Asaf Varol, Murat Karabatak, Cihan Varol, Ahad Nasab |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
ISBN (Electronic) | 9798350336986 |
DOIs | |
State | Published - 2023 |
Event | International Symposium on Digital Forensics and Security - Chattanooga, United States Duration: May 11 2023 → May 12 2023 Conference number: 11 https://ieeexplore.ieee.org/servlet/opac?punumber=10131120 |
Publication series
Name | ISDFS 2023 - 11th International Symposium on Digital Forensics and Security |
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Conference
Conference | International Symposium on Digital Forensics and Security |
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Abbreviated title | ISDFS |
Country/Territory | United States |
City | Chattanooga |
Period | 05/11/23 → 05/12/23 |
Internet address |
Scopus Subject Areas
- Artificial Intelligence
- Computer Networks and Communications
- Information Systems
- Information Systems and Management
- Safety, Risk, Reliability and Quality
Keywords
- BERT
- deep learning
- Distil-BERT
- Natural Language Processing
- web attack
- web attack detection