SCIENCE@home

Web-Based Deep Learning Model for Zero Day Vulnerability Detection using FastAPI

C.V. Suresh babu,V. Surendar,E. Sriram,S. Subhash

Published 2024 in 2024 International Conference on Advances in Data Engineering and Intelligent Computing Systems (ADICS)

ABSTRACT

In the evolving world of technology, the growing number of security weaknesses is becoming a serious concern, for our online safety. This research presents a method leveraging LSTM technology to identify both familiar and undiscovered vulnerabilities in log files of web application frameworks. The platform features a user interface developed using ReactJS with upcoming upgrades focused on enhancing appeal and simplifying the log file upload process, for vulnerability assessment. By enabling users to effortlessly upload logs the system creates reports that outline forms of attacks offering valuable insights, into possible security risks. Recent live testing of the system showed its ability to quickly identify threats showcasing its potential as a proactive tool for enhancing cybersecurity defenses. Moreover, the model's prediction accuracy stands at a 98% proving its strength and dependability in identifying weaknesses. These results underscore the significance of security measures, in dealing with the evolving landscape of digital threats.

PUBLICATION RECORD

  • Publication year

    2024

  • Venue

    2024 International Conference on Advances in Data Engineering and Intelligent Computing Systems (ADICS)

  • Publication date

    2024-04-18

  • Fields of study

    Not labeled

  • Identifiers
    DOI 10.1109/ADICS58448.2024.10533540
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

CITATION MAP

EXTRACTION MAP

CLAIMS

  • No claims are published for this paper.

CONCEPTS

  • No concepts are published for this paper.

REFERENCES

CITED BY

Showing 1-5 of 5 citing papers · Page 1 of 1