Article Per Year (5 Year)
p-Index From 2021 - 2026
0.339
P-Index
This Author published in this journals
All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control)
N. Subhashini
Vellore Institute of Technology
Computer Science & IT

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Deep learning based phishing website detection N. Subhashini; Amogh Banerjee; Abhi Kumar; S. Muthulakshmi; S. Revathi
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 22, No 1: February 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v22i1.25516

Abstract

Phishing attacks use fraudulent websites that trick people into disclosing sensitive information. More effective and precise methods are required to identify phishing websites so that people and organisations can be protected from the damaging effects of these online threats. The aim of this work is to develop a model that can identify phishing uniform resource locator (URLs) more accurately than current approaches while requiring less training time, testing time, and storage space. This research work proposes a novel method for identifying phishing websites using a long short-term memory (LSTM) gated recurrent unit (GRU) algorithm to detect phishing URLs. The accuracy of the suggested method is 98.89%, which is significantly better than the findings of earlier studies. The model also showed a need for shorter training and testing time, and a reduced amount of storage space.
Co-Authors Abhi Kumar Amogh Banerjee S. Muthulakshmi S. Revathi