Machine Learning Based Malicious URL Detection (original) (raw)

Abstract

Today Internet technology has become an essential part of our life for education, entertainment, gaming, banking and communication. In this modern digital era, it is very easy to have any information by one click. But everything which has pros and cons, as we have any information at our tips but Internet is an attack platform also. When we use Internet to make our work easy same time many attacker try to steal information from our system. There are many means for attacking, malicious URL one of them. When a user visits a website, which is malicious then it triggers a malicious activity which is predesigned. Hence, there are various approaches to find dangerous URL on the Internet. In this paper, we are using machine learning approach to detect malicious URLs. We used ISCXURL2016 dataset and used J48, Random forest, Lazy algorithm and Bayes net classifiers. As performance metrics, we calculate accuracy, TPR, FPR, precision and recall.

Loading...

Loading Preview

Sorry, preview is currently unavailable. You can download the paper by clicking the button above.

References (15)

  1. D. Sahoo, C. Liu, S.C.H. Hoi, "Malicious URL Detection using Machine Learning: A Survey", CoRR, 2017.
  2. McGrath, D. Kevin, and Minaxi Gupta, "Behind Phishing: An Examination of Phisher Modi Operandi", LEET 8 (2008): 4.
  3. Kevin, M.D., Gupta, M, "Behind phishing: an examination of phisher modi Operandi", In: Proceedings of the 1st Usenix Workshop on Large-Scale Ex-ploits and Emergent Threats (2008)
  4. Vanhoenshoven, Frank, et al. "Detecting malicious URLs using machine learning techniques.", IEEE Symposium Series on Computational Intelligence (SSCI), 2016.
  5. P. Zhao and S. C. Hoi, "Cost-sensitive online active learning with application to malicious URL detection," in Proceedings of the 19 th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2013, pp. 919-927.
  6. Cho Do Xuan, Hoa Dinh Nguyen, Tisenko Victor Nikolaevich, "Malicious URL Detection based on Machine Learning", International Journal of Advanced Computer Science and Applications, Vol. 11, No. 1, 2020
  7. Sayamber, Anjali B., and Arati M. Dixit, "Malicious URL detection and identification", International Journal of Computer Applications 99.17 (2014): 17-23.
  8. Shi, Y., Chen, G. & Li, J, "Malicious Domain Name Detection Based on Extreme Machine Learning", Neural Process Lett 48, 1347-1357 (2018).
  9. Baojiang Cui, Shanshan He, Xi Yao, "Malicious URL detection with feature extraction based on machine learning", "International Journal of High-Performance Computing and Networking", Volume 12, Issue 2.
  10. WU, Chun-ming, "Malicious website detection based on URLs static features.", DEStech Transactions on Computer Science and Engineering mso (2018).
  11. Chong, Christophe, Daniel Liu, and Wonhong Lee. "Malicious url detection." (2009).
  12. Ma, Justin, Lawrence K. Saul, Stefan Savage, Geoffrey M. Voelker," Beyond Blacklists: Learning to Detect Malicious Web Sites from Suspicious URLs." , http://www.cs.berkeley.edu/ jtma/papers/beyondbl-kdd2009.pdf.
  13. Mohammad Saiful Islam Mamun, Mohammad Ahmad Rathore, Arash Habibi Lashkari, Natalia Stakhanova and Ali A. Ghorbani, "Detecting Malicious URLs Using Lexical Analysis", Network and System Security, Springer International Publishing, P467--482, 2016.
  14. Deshmukh J.J. And Tated R.R.," Weka -Open Source Technology, Its Implementation and Benefits", World Research Journal of Computer Architecture, Volume 1, Issue 1, 2012, pp.-01-05.
  15. Kaushik H. Raviya, Biren Gajjar," Performance Evaluation of Different Data Mining Classification Algorithm Using WEKA".