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International Journal of Computer Engineering in Research Trends. Scholarly, Peer-Reviewed,Open Access and Multidisciplinary

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Detection of Malicious URLs using Artificial Intelligence

Monisha.T, Sridevi.R, Tirumalini.K.R, ,
Computer Science and Engineering, S.A. Engineering College, Anna University, Chennai, India

Background/Objectives: The main objective of the project is to avoid various security threats and network attacks by detecting malicious Uniform Resource Locator(URL) based on the keyword text classification. Methods/Statistical analysis: A semi-supervised technique, naive Bayes classification is proposed to locate malicious URL by text classification phenomena. The probabilities of the predicted and the exact values are calculated, and it results with high probability. With more accuracy, the malignant URL is predicted. A page rank algorithm is used to detect the blacklist which contains the URLs that are already noted as spam, malware or phishing URL. Findings: With the persistent improvement of Web assaults, many web applications have been languishing from different types of security dangers and system assaults. The security identification of URLs has consistently been the focal point of Web security. One of the main sources of attacks is via malicious URLs; the attackers may send embedding executable codes or injects malicious codes through these URLs. Thus, it is important to improve the unwavering quality and security of web applications by precisely identifying malignant URLs. The utilization of profound figuring out how to group URLs to recognize Web guests' aims has significant hypothetical and scientific values for Web security investigate, giving new plans to canny security discovery.

Monisha.T,Sridevi.R,Tirumalini.K.R."Detection of Malicious URLs using Artificial Intelligence". International Journal of Computer Engineering In Research Trends (IJCERT) ,ISSN:2349-7084 ,Vol.7, Issue 08,pp.6-10, August - 2020, URL :,

Keywords : Link analysis, malignant code, malignant URL datasets, naive Bayes classification.

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[2] Bo Feng; Qiang Fu; Mianxiong Dong; Dong Guo; Qiang Li "Multistage and Elastic Spam Detection in Mobile Social Networks through Deep Learning"IEEE Network ( Volume: 32 , Issue: 4 , July/August 2018 )
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[11] Yuguang Huang ; Lei Li"Naive Bayes classification algorithm based on small sample set" IEEE 2011 IEEE International Conference on Cloud Computing and Intelligence Systems
[12] HaiyiZhang; Di Li"Naïve Bayes Text Classifier" IEEE 2007 IEEE International Conference on Granular Computing (GRC 2007)
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[14] Justin Ma: Lawrence K. Saul: Stefan Savage: Geoffrey M. Voelker:“Identifying Suspicious URLs: An Application of Large-Scale Online Learning” 26th International Conference on Machine Learning, Montreal, Canada, 2009.
[15] Training Datasets:

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