CLASSIFICATION OF PHISHING ATTACKS IN SOCIAL MEDIA USING ASSOCIATIVE RULE MINING AUGMENTED WITH FIREFLY ALGORITHM
Abstract
Social media has significantly grown as a preferred medium of communication for individuals and groups. It is also a tool for disseminating information to the public. Social media offers several advantages, most especially contacting millions of people at the same time. Social media attacks such as phishing evolved as a result of messaging and disseminating capabilities of social media network sites. This challenge of continuous attacks has attracted the attention of many researchers to propose different techniques to detect and classify both phishing attacks and legitimate messages. Studies in the literature revealed that some of the models proposed for phishing attacks may not be perfect to stop adversaries and, there are still different phishing attacks that hindered the robust nature of social media. This study proposed associative rule mining augmented with the Firefly algorithm which attained a high degree of accuracy in both phishing attack messages and legitimate messages.
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References
2. DiMicco, J., Millen, D. R., Geyer, W., Dugan, C., Brownholtz, B., & Muller, M. (2008, November). Motivations for social networking at work. In Proceedings of the 2008 ACM conference on Computer supported cooperative work (pp. 711-720).
3. Mohammad, R., Thabtah, F. & Mccluskey, T. (2013). Predicting phishing websites based on self-structuring neural network. Neural Computing and Applications, 25, 443–458.
4. Taylor, C. (2010). Integrated marketing communications in 2010 and beyond. International Journal of Advertising, 29 (2), 161-164.
5. Boyd, D.M. & Ellison, N.B. (2007). Social network sites: Definition, history, and scholarship. Journal of CMC, 13(1), 210-230.
6. Trisha, D. B., (2012). Effectiveness of social media as a tool of communication and its potential for technology enabled connections: A micro-level study. International Journal of Scientific and Research Publications, 2(5),1-10.
7. Verma, R. & Das, A. (2017). What’s in a URL: Fast feature extraction and malicious URL detection? In Proceedings of the 3rd Association for Computing Machinery (ACM) on International Workshop on Security And Privacy Analytics, IWSPA ’17, (New York, NY, USA), pp 55–63.
8. Abad, C., (2005). The economy of phishing: A survey of the operations of the phishing market. Retrieved May, 29 2023 from: https://firstmonday.org/ojs/index.php/fm/article/view/1272/1192
9. Aman, R. Tarun, K. & Dhan (2019). Phish-Defense: Phishing Detection Using Deep Recurrent Neural Networks. ACM Cryptography and security, Cornel University, Retrieved May 9, 2023 from: https://doi.org/10.48550/arXrv.2110.13424
10. Faisal-Khana M. D. & Ranab, B. L. (2021). Detection of Phishing Websites Using Deep Learning Techniques. Turkish Journal of Computer and Mathematics Education. 12(10), 3880-3892
11. Odukoya, O.H. (2017). A social network spam detection model. International Journal of Scientific Engineering and Research, 8, 11
12. Zuraiq, A. A., & Alkasassbeh, M. (2019, October). Phishing detection approaches. In 2019 2nd International Conference on new Trends in Computing Sciences (ICTCS) (pp. 1-6). IEEE.
13. Ali, W. (2017). Phishing website detection based on supervised machine learning with wrapper features selection. International Journal of Advanced Computer Science and Applications, 8(9).
14. Da-Silva, C. M. R., Feitosa, E. L. & Garcia, V. C. (2020). Heuristic-based strategy for Phishing prediction: A survey of URL-based approach. Computational Security. 88(pp. 101613).
15. Tao, F., & Chuan, Y., (2020). Visualizing and Interpreting RNN Models in URL-based Phishing Detection. Session 1: Assessment and Detection of Security Threats, SACMAT ’20, June 10–12, 2020, Barcelona, Spain
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