A Survey on Detecting Suspicious and Malicious URLs in E-mail and Social Networks

Authors

  • Dhanashri V Department, Of Computer Engineering, Savitribai Phule Pune University, Maharashtra, India
  • Bhagyashri V Department, Of Computer Engineering, Savitribai Phule Pune University, Maharashtra, India
  • Monika S Department, Of Computer Engineering, Savitribai Phule Pune University, Maharashtra, India
  • Seema N Department, Of Computer Engineering, Savitribai Phule Pune University, Maharashtra, India

Keywords:

Social network, URL detection, Bayesian classification, Decision tree, Feature set extraction

Abstract

These days, Email is also one of the advertising medium. Though it is a healthy medium for advertising, this is getting misused also. It gets really inconvenient to attend all those unnecessary emails. It is also very distracting. Here we are proposing a solution as email classifier. It will classify the inbox emails into various categories. A selected category of emails can be blocked considering it spam. In this study, the features of traditional heuristics and social networking are presented by combining them in feature set. This is done with Bayesian algorithm, know very helpful in such text classification tasks. The experimental result shows that the high detection rate is achieved by proposed approach. In this by using reduced feature set method we identify malicious URLs in email.

References

Chia-Mei Chen, D.J. Guan, Qun-Kai Su, National Sun Yat-sen University, Kaohsiung, Taiwan, ROC.Feature set identification for detecting suspicious URLs using Bayesian classification in social networks, 133-147, 2014.

Dhanalakshmi ranganayakulu, Chellappan C, “Adhiparasakthi Engineering College, Melmaruvathur 603319, INDIA. Anna University, Chennai 600025, INDIA. Detecting malicious URLs in E-mail-An implementation, 125-131, 2013

Lei SHI, Qiang WANG, Xinming MA, Mei WENG, Hongbo QIAO, College of Information and Management Science, HeNan Agricultural University, Zhengzhou 450002,China.Spam Email Classification Using Decision Tree Ensemble, 949-956, 2012

Enrico Blanzieri University of Trento, Italy Anton Bryl, Italy Create-Net, Trento, Italy. A Survey of Learning-Based Techniques of Email Spam Filtering, 1-35, October 2007.

Xin Jin et.al “Social Spam Guard: A Data Mining Based Spam Detection System for Social Media Networks”, 37thInternational Conference on Very Large Data Bases, August 29th 2011, Washington.

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Published

2025-11-10

How to Cite

[1]
V. Dhanashri, V. Bhagyashri, S. Monika, and N. Seema, “A Survey on Detecting Suspicious and Malicious URLs in E-mail and Social Networks”, Int. J. Comp. Sci. Eng., vol. 3, no. 9, pp. 205–209, Nov. 2025.

Issue

Vol. 3 No. 9 (2015): IJCSE September Edition

Section

Survey Article

License

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