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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM42507
Volume:
9 |
3 |
Mar 2025
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.
â Phishing attacks remain a significant threat to cybersecurity, targeting individuals and organizations alike. This paper introduces PHISHNET, a comprehensive threat intelligence system that leverages machine learning techniques to detect and mitigate phishing attacks across two communication vectors: URLs, emails. The system employs Gradient Boosting for URL detection, achieving an accuracy of approximately 98%, while Random Forest algorithms are utilized for email, attaining accuracies of approximately 97% and 96%, respectively. By analyzing various features related to URLs and extracting content from emails, PHISHNET provides users with actionable insights to enhance their security posture against phishing threats.
KEYWORDS
Publication Date
1/9/2025
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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