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

Vispute Dhanashri1 , Vispute Bhagyashri2 , Sonawane Monika3 , Nikam Seema4

Section:Survey Paper, Product Type: Journal Paper
Volume-3 , Issue-9 , Page no. 205-209, Sep-2015

Online published on Oct 01, 2015

Copyright © Vispute Dhanashri, Vispute Bhagyashri, Sonawane Monika, Nikam Seema . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: Vispute Dhanashri, Vispute Bhagyashri, Sonawane Monika, Nikam Seema, “A Survey on Detecting Suspicious and Malicious URLs in E-mail and Social Networks,” International Journal of Computer Sciences and Engineering, Vol.3, Issue.9, pp.205-209, 2015.

MLA Style Citation: Vispute Dhanashri, Vispute Bhagyashri, Sonawane Monika, Nikam Seema "A Survey on Detecting Suspicious and Malicious URLs in E-mail and Social Networks." International Journal of Computer Sciences and Engineering 3.9 (2015): 205-209.

APA Style Citation: Vispute Dhanashri, Vispute Bhagyashri, Sonawane Monika, Nikam Seema, (2015). A Survey on Detecting Suspicious and Malicious URLs in E-mail and Social Networks. International Journal of Computer Sciences and Engineering, 3(9), 205-209.

BibTex Style Citation:
@article{Dhanashri_2015,
author = {Vispute Dhanashri, Vispute Bhagyashri, Sonawane Monika, Nikam Seema},
title = {A Survey on Detecting Suspicious and Malicious URLs in E-mail and Social Networks},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {9 2015},
volume = {3},
Issue = {9},
month = {9},
year = {2015},
issn = {2347-2693},
pages = {205-209},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=670},
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=670
TI - A Survey on Detecting Suspicious and Malicious URLs in E-mail and Social Networks
T2 - International Journal of Computer Sciences and Engineering
AU - Vispute Dhanashri, Vispute Bhagyashri, Sonawane Monika, Nikam Seema
PY - 2015
DA - 2015/10/01
PB - IJCSE, Indore, INDIA
SP - 205-209
IS - 9
VL - 3
SN - 2347-2693
ER -

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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.

Key-Words / Index Term

Social Network, URL Detection, Bayesian Classification, Decision Tree, Feature Set Extraction

References

[1] 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.
[2] 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
[3] 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
[4] 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.
[5] 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.