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ISSN Approved Journal No: 2320-2882 | Impact factor: 7.97 | ESTD Year: 2013
Call For Paper - Volume 14 | Issue 3 | Month- March 2026
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Published Paper Details:
Paper Title
PhishNet: Machine Learning-Powered Detection of Malicious Websites
Authors
Suguna Syamala Gantla, Poojitha Kolaparthi, Jampana Sri Naga Sai Srija, Jampani Kousikee Krishna Valli, Syam Kumar Savaram
Keywords
Malicious URLs, Cyber threats, Machine learning, Detection accuracy, Blacklisting, Lexical characteristics, Host-based attributes, Content-based features, Decision trees, Support vector machines (SVM), Deep learning techniques, Security measures.
Abstract
Cyber risks have increased due to the exponential development in internet usage, especially through malicious URLs. Blacklisting is an example of a traditional security mechanism that is unable to keep up with changing threats. Through the analysis of many aspects, including lexical, host-based, and content-based characteristics, this study investigates a machine learning-based method for identifying malicious URLs. The study assesses various machine learning models to improve detection accuracy, such as support vector machines, decision trees, and deep learning approaches. When compared to traditional methods, the results show that machine learning techniques greatly increase the efficiency and accuracy of harmful URL identification.
IJCRT's Publication Details
Unique Identification Number - IJCRT2503037
Paper ID - 278111
Page Number(s) - a301-a305
Pubished in - Volume 13 | Issue 3 | March 2025
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Publisher Name - IJCRT | www.ijcrt.org | ISSN : 2320-2882
E-ISSN Number - 2320-2882
Cite this article
Suguna Syamala Gantla, Poojitha Kolaparthi, Jampana Sri Naga Sai Srija, Jampani Kousikee Krishna Valli, Syam Kumar Savaram, "PhishNet: Machine Learning-Powered Detection of Malicious Websites", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.13, Issue 3, pp.a301-a305, March 2025, Available at :http://www.ijcrt.org/papers/IJCRT2503037.pdf
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Volume 14 | Issue 3
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Submit Manuscript Online Impact Factor: 7.97 Review Results : Within 02-03 Days Paper Publication : Within 02-03 Days
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ISSN: 2320-2882 Impact Factor: 7.97 and ISSN APPROVED Journal Starting Year (ESTD) : 2013
ISSN and 7.97 Impact Factor Details
ISSN: 2320-2882 Impact Factor: 7.97 and ISSN APPROVED Journal Starting Year (ESTD) : 2013
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