International Journal of Science and Research (IJSR)
Since Year 2012 | Open Access | Fully Refereed | Peer Reviewed

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Research Paper | Computer Science & Engineering | India | Volume 4 Issue 12, December 2015

Enhanced Framework for Detecting Malicious HTTP Redirections with Supporting Classifier

Thella Vineela | Bondili Balvinder Singh ^[2]

Abstract: Malicious URL detection has become increasingly difficult due to the evolution of phishing campaigns and efforts to avoid weakening blacklists. The existing state of cybercrime has allowed pirates to host campaigns with smaller lifespan, which reduces the efficacy of the backlist. At the same time, standard supervised learning algorithms are known to generalize in specific patterns observed in the training data, which makes them a better alternative against piracy campaigns. However the highly dynamic environment of these campaigns requires models updated frequently, which poses new challenge as most learning algorithms are too computationally require exclusive retraining. This paper surveys two contributions. Firstly it discusses the problems associated with Malicious URL and there propagation mechanism. Secondly, it provides method to detect and distinguish Malicious URL by analyzing them. For analysis Recall, Precision and F-measures matrices are used.

Keywords: Attacks, Adware Classification, Malicious web page analysis, Malicious URLs, Machine Learning

Edition: Volume 4 Issue 12, December 2015,

Pages: 112 - 116

Enhanced Framework for Detecting Malicious HTTP Redirections with Supporting Classifier

How to Cite this Article?

Thella Vineela, Bondili Balvinder Singh, "Enhanced Framework for Detecting Malicious HTTP Redirections with Supporting Classifier", International Journal of Science and Research (IJSR), https://www.ijsr.net/get_abstract.php?paper_id=SUB158823, Volume 4 Issue 12, December 2015, 112 - 116, #ijsrnet

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