An Ideal Approach for Detection of Phishing Attacks using Naïve Bayes Classifier

  IJCTT-book-cover
 
International Journal of Computer Trends and Technology (IJCTT)          
 
© 2016 by IJCTT Journal
Volume-40 Number-2
Year of Publication : 2016
Authors : R.Priya
  10.14445/22312803/IJCTT-V40P115

MLA

R.Priya "An Ideal Approach for Detection of Phishing Attacks using Naïve Bayes Classifier". International Journal of Computer Trends and Technology (IJCTT) V40(2):84-87, October 2016. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.

Abstract -
Phishing attack is an aberrant trick to peculate user’s private information by duping them to assail via a spurious website planned to mimic and resembles as an authentic website. The user’s confidential information such as username, password, and PIN number will be grabbed by the attacker and creates a fraudulent transactions. The information holder’s credentials as well as money will be seized. The phishing and legitimate website will have high intelligible resemblances by which the attacker will seize the credentials of the user. Inorder to detect the phishing attacks there exists various techniques such as blacklisting, whitelisting, heuristics and machine learning. Nowadays machine learning is used and found to be more effective. The proposed system extracts the source code features, URL features and image features from the phishing website. The features that are extracted are given to the ant colony optimization algorithm to acquire the reduced features. The reduced features are again given to the Naïve Bayes classifier inorder to classify the webpage as genuine or phished.

References
[1] Mayank Pandey, Vadlamani Ravi, Text and Data Mining to Detect Phishing Websites and Spam Emails, Swarm, Evolutionary, and Memetic Computing, Bijaya Ketan Panigrahi, Ponnuthurai Nagaratnam Suganthan, Swagatam Das, Shubhransu Sekhar Dash Eds., Springer International Publishing: Springer, 2013.
[2] Choon Lin Tan, Kang Leng Chiew, San Nah Sze , Phishing Webpage Detection Using Weighted URL Tokens for Identity Keywords Retrieval in 9th International Conference on Robotic, Vision, Signal Processing and Power Applications, Haidi Ibrahim, Shahid Iqbal, Soo Siang Teoh, Mohd Tafir Mustaffa Eds., Springer Singapore, 2017.
[3] Zhijun Yan, Su Liu, Tianmei Wang, Baowen Sun, Hansi Jiang, Hangzhou Yang, A Genetic Algorithm Based Model for Chinese Phishing E-commerce Websites Detection in HCI in Business, Government, and Organizations: eCommerce and Innovation, Fiona Fui-Hoon Nah, Chuan- Hoo Tan, Springer International Publishing, 2016.
[4] Yuancheng Li, Rui Xiao, Jingang Feng, Liujun Zhao, “A semi-supervised learning approach for detection of phishing webpages,” Optik-International Journal for Light and Electron Optics, vol.124, Issue 23, December 2013.
[5] Chia-Mei Chen, D.J. Guan, Qun-Kai Su, “Feature set identification for detecting suspicious URLs using Bayesian classification in social networks,” Information Sciences, vol.289, December 2014.
[6] P.A. Barraclough, M.A. Hossain, M.A. Tahir, G. Sexton, N. Aslam, Intelligent phishing detection and protection scheme for online transactions, Expert Systems with Applications, vol. 40, Issue 11, September 2013.
[7] Xianghan Zheng, Zhipeng Zeng, Zheyi Chen, Yuanlong Yu, Chunming Rong, “Detecting spammers on social networks,” Neurocomputing, vol. 159, pp. 27-34, July 2015.
[8] Maher Aburrous, M.A. Hossain, Keshav Dahal, Fadi Thabtah, “Intelligent Phishing Detection System for e- Banking Using Fuzzy Data Mining,” Expert Systems with Applications, vol. 37, pp. 913-7921, December 2010. [9] Gaurav Gupta, Josef Pieprzyk, “Socio-technological phishing prevention,”Information Security Technical Report, vol. 16, Issue 2, May 2011.
[10] Ram B. Basnet, Andrew H. Sung, Quingzhong Liu, “Feature Selection for Improved Phishing Detection” in Advanced Research in Applied Artificial Intelligence: Proc. of the 25th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2012, Dalian, China, June 9-12, 2012, He Jiang, Wei Ding ,Moonis Ali, Xindong Wu, Eds. Berlin: Springer, 2012.
[11] https://securelist.com/analysis/quarterly-spamreports/ 74682/spam-and-phishing-in-q1-2016/.

Keywords
Phishing, Ant colony Optimization, Naïve Bayes Classifier, Feature Extraction.