Detection of Web-Attack using DistilBERT, RNN, and LSTM

Biodoumoye George Bokolo, Lei Chen, Qingzhong Liu

Research output: Contribution to book or proceedingConference articlepeer-review

9 Scopus citations

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 languageEnglish
Title of host publicationISDFS 2023 - 11th International Symposium on Digital Forensics and Security
EditorsAsaf Varol, Murat Karabatak, Cihan Varol, Ahad Nasab
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350336986
DOIs
StatePublished - 2023
EventInternational Symposium on Digital Forensics and Security - Chattanooga, United States
Duration: May 11 2023May 12 2023
Conference number: 11
https://ieeexplore.ieee.org/servlet/opac?punumber=10131120

Publication series

NameISDFS 2023 - 11th International Symposium on Digital Forensics and Security

Conference

ConferenceInternational Symposium on Digital Forensics and Security
Abbreviated titleISDFS
Country/TerritoryUnited States
CityChattanooga
Period05/11/2305/12/23
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

Fingerprint

Dive into the research topics of 'Detection of Web-Attack using DistilBERT, RNN, and LSTM'. Together they form a unique fingerprint.

Cite this