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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM5861
Volume:
4 |
9 |
Sep 2020
Pages:
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
The exponential increase in phishing assaults has led to significant financial losses for both individuals and companies, and it has also raised questions regarding the security and privacy of their personal information. The notable annual increase in phishing attempts suggests that the existing detection methods are inadequate, underscoring the need for the development of more robust and effective phishing detection techniques. This paper introduces an extension model for phishing detection employing machine learning, with the aim of enhancing both efficacy and accuracy in identifying phishing attempts. The survey conducted in this paper delves into the current state-of-the-art techniques in phishing detection, highlighting their limitations. Furthermore, it puts forth a novel approach in the developmental stages, centered around feature extraction from malicious URLs using machine learning.
KEYWORDS
Publication Date
11/20/2023
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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