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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM64883
Volume:
10 |
6 |
Jun 2026
Pages:
1-7
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.
Master Of Computer Applications
GIET ENGINEERING COLLEGE
Master Of Computer Applications
GIET ENGINEERING COLLEGE
Phishing attacks have become one of the most significant cybersecurity threats, causing financial losses and unauthorized access to sensitive information. Traditional phishing detection methods mainly rely on blacklists and rule-based techniques, which are ineffective against newly generated phishing websites. This paper presents a Machine Learning-Based Trust Scoring and Real-Time Detection of Phishing Websites Using URL Intelligence framework to identify malicious websites efficiently. The proposed system analyzes various URL-based features, including URL length, domain age, SSL certificate status, suspicious keywords, and subdomain information. These features are processed using ensemble machine learning algorithms such as Random Forest and XGBoost to classify websites as legitimate or phishing. In addition to classification, the system generates a trust score ranging from 0 to 100, helping users assess the credibility of a website. The results are displayed through a color-coded interface indicating safe, suspicious, or dangerous websites. The framework supports real-time URL analysis and provides fast, accurate predictions. Experimental results demonstrate that the proposed approach effectively detects phishing websites, including previously unseen attacks, while improving user awareness and online security.
KEYWORDS
The system successfully analyzes website URLs and uses machine learning techniques to determine whether a website is safe or potentially dangerous. By generating a trust score and displaying prediction results in an easy-to-understand format, the application helps users make informed decisions while browsing the internet. The user authentication, URL prediction, result display, and history tracking modules work together to provide a smooth and reliable user experience. The testing results showed that the system was able to identify phishing websites effectively and maintain a record of all previous analyses. Overall, the project demonstrates how machine learning and URL intelligence can be used to improve online security and protect users from phishing attacks.
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Publication Date
6/12/2026
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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No funding information available for this research.
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