Algorithm for detecting illegal links using the association rule for improving the web attack detection accuracy of web application firewall

Nguyen Manh Thang

Abstract


Illegal links appear more often through social networks with "dizzying" speed. When users click on a "malicious" link it can bring them potential danger. One of the most popular social networks is Facebook. It is one of the ways for hackers to share malicious links. For example, there are many advertisements with links, and when the user clicks on these links all the information of the user practically falls into the hands of hackers. Hence, a system administrator needs to check requests before running them on the server to ensure security. One of the most common approaches is the Web Application Firewall (WAF). The article presents an algorithm for detecting illegal links based on tf-idf technology for evaluating the "importance" of keywords, symbols in the links of user requests from the user's browser with machine learning method to improve the accuracy of identifying illegal links.


Full Text:

PDF

References


Han Byeong Woo, Yoon Ji Won. Illegal and Harmful Information Detection Technique Using Combination of Search Words // Journal of the Korea Institute of Information Security and Cryptology. – 2016. – Vol. 26, no. 2. – P. 397–404.

Sampat Hemali, Saharkar Manisha, Pandey Ajay, Lopes Hezal. Detection of Phishing Website Using Machine Learning. – 2018.

Gupta Abhishek, Jain Ankit, Yadav Samartha, Taneja Harsh. Literature Survey on Detection of Web Attacks Using Machine Learning. – 2018.

Kim Tae Ghyoon, Choi Young Han, Choi Seok Jin, Lee Cheol Won. System and method for detecting malicious script. – 5 12/2015. – US Patent 9,032,516.

Li Zhi–Yong, Tao Ran, Cai Zhen–He, Zhang Hao. A web page malicious code detect approach based on script execution // Natural Computation, 2009. ICNC’09. Fifth International Conference on. Vol. 6. – IEEE. 2009. – P. 308–312.

Chitra S, Jayanthan K, Preetha S, Shankar RN Uma. Predicate based algorithm for malicious web page detection using genetic fuzzy systems and support vector machine // International Journal of Computer Appli¬cations. – 2012. – Vol. 40, no. 10. – P. 13–19.

Shahriar Hossain, Zulkernine Mohammad. Trustworthiness testing of phish¬ing websites: A behavior model–based approach // Future Generation Computer Systems. – 2012. – Vol. 28, no. 8. – P. 1258–1271.

Irani Danesh, Webb Steve, Giffin Jonathon, Pu Calton. Evolutionary study of phishing // ECrime Researchers Summit, 2008. – IEEE. 2008. – P. 1–10.

Lifshits Yuri. Support Vector Method // URL: http://logic.pdmi.ras.ru/~ yura/internet/07ia.pdf. - 2006.

Vorontsov KV. Lectures on the support vector method // Computing Center of the Russian Academy of Sciences, Moscow: URL: http://www.ccas.ru/voron/download/SVM.pdf (accessed: 03.03.12). - 2007.

Stasyuk AI, Korchenko AA. A method for identifying anomalies generated by cyberattacks in computer networks // Zahist shformatsl. - 2012. - T. 4, No. 57. - S. 127–132.

Golovko VA, Bezobrazov SV. Design intelligent anomaly detection systems. - 2011.

Petrenko Sergey Anatolyevich. Methods for detecting intrusions and anomalies in the functioning of cybersystems // Transactions of the Institute for System Analysis of the Russian Academy of Sciences. - 2009. - T. 41. - S. 194–202.

Appelt Dennis, Nguyen Cu D, Panichella Annibale, Briand Lionel C. A machine learning driven evolutionary approach for testing web application firewalls // IEEE Transactions on Reliability. – 2018. – Vol. 67, no. 3 – P. 733–757.

Ma Justin, Saul Lawrence K, Savage Stefan, Voelker Geoffrey M. Beyond blacklists: learning to detect malicious web sites from suspicious URLs // Proceedings of the 15th ACM SIGKDD international conference on Knowl¬edge discovery and data mining. – ACM. 2009. – P. 1245–1254.

Basnet Ram, Mukkamala Srinivas, Sung Andrew H. Detection of phishing attacks: A machine learning approach // Soft Computing Applications in Industry. – Springer, 2008. – P. 373–383.

Sahoo Doyen, Liu Chenghao, Hoi Steven CH. Malicious URL detection using machine learning: A survey // arXiv preprint arXiv:1701.07179. – 2017.

Yadav BV Ram Naresh, Satyanarayana B, Vasumathi D. A Vector Space Model Approach for Web Attack Classification Using Machine Learning Technique // Proceedings of the Second International Conference on Com¬puter and Communication Technologies. – Springer. 2016. – P. 363–¬373.


Refbacks

  • There are currently no refbacks.


Abava  Absolutech Fruct 2020

ISSN: 2307-8162