International Journal For Multidisciplinary Research

E-ISSN: 2582-2160     Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 6 Issue 3 May-June 2024 Submit your research before last 3 days of June to publish your research paper in the issue of May-June.

Enhancing Cybersecurity Through AI-Driven Threat Detection: A Transfer Learning Approach

Author(s) E Satya Vinayak, Mr. K Anbuthiruvarangan, Kudupudi Chakradhar, Anbudoss P
Country India
Abstract In today's digital landscape, fortifying cyber security is of utmost importance.
This research introduces an innovative strategy that relies on transfer learning within deep neural networks to combat evolving threats, with a specific focus on phishing URLs and Malicious links, a major vector for cyber-attacks.
We meticulously curate a diverse dataset of phishing and legitimate URLs, subjecting it to rigorous pre-processing.
Departing from traditional methods, we leverage transfer learning to extract intricate patterns within URLs and their content.
Our unique approach integrates transfer learning into a hybrid model, combining deep learning techniques with the power of transfer learning.
This hybrid model employs soft and hard voting to optimize phishing threat detection accuracy and efficiency.
We fine-tune our models with advanced feature selection and hyper parameter optimization, using rigorous evaluation metrics to assess performance
Field Computer
Published In Volume 6, Issue 3, May-June 2024
Published On 2024-05-05
Cite This Enhancing Cybersecurity Through AI-Driven Threat Detection: A Transfer Learning Approach - E Satya Vinayak, Mr. K Anbuthiruvarangan, Kudupudi Chakradhar, Anbudoss P - IJFMR Volume 6, Issue 3, May-June 2024. DOI 10.36948/ijfmr.2024.v06i03.18022
DOI https://doi.org/10.36948/ijfmr.2024.v06i03.18022
Short DOI https://doi.org/gttbjx

Share this