Abstract
In this paper, the malicious URLs detection is treated as a binary classification problem and performance of several well-known classifiers are tested with test data. The algorithms Random Forests and support Vector Machine (SVM) are studied in particular which attain a high accuracy. These algorithms are used for training the dataset for classification of good and bad URLs. The dataset of URLs is divided into training and test data in 60:40, 70:30 and 80:20 ratios. Accuracy of Random Forests and SVMs is calculated for several iterations for each split ratio. According to the results, the split ratio 80:20 is observed as more accurate split and average accuracy of Random Forests is more than SVMs. SVM is observed to be more fluctuating than Random Forests in accuracy.
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References
Choi, H., Zhu, B.B., Lee, H.: Detecting malicious web links and identifying their attack types. WebApps 11, 11 (2011)
Gabriel, A.D., Gavrilut, D.T., Alexandru, B.I., Stefan, P.A.: Detecting malicious URLs: a semi-supervised machine learning system approach. In: 2016 18th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC), pp. 233–239. IEEE (2016)
Huang, D., Xu, K., Pei, J.: Malicious URL detection by dynamically mining patterns without pre-defined elements. World Wide Web 17(6), 1375–1394 (2014)
Liu, C., Wang, L., Lang, B., Zhou, Y.: Finding effective classifier for malicious URL detection. In: Proceedings of the 2018 2nd International Conference on Management Engineering, Software Engineering and Service Sciences, ICMSS 2018, pp. 240–244. ACM, New York (2018)
Ma, J., Saul, L.K., Savage, S., Voelker, G.M.: Learning to detect malicious URLs. ACM Trans. Intell. Syst. Technol. (TIST) 2(3), 30 (2011)
Narendra, P.: Malicious URL detection. http://athena.ecs.csus.edu/narendrp/project.html
Vanhoenshoven, F., Nápoles, G., Falcon, R., Vanhoof, K., Köppen, M.: Detecting malicious URLs using machine learning techniques. In: 2016 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1–8. IEEE (2016)
Verma, R., Das, A.: What’s in a URL: fast feature extraction and malicious URL detection. In: Proceedings of the 3rd ACM on International Workshop on Security and Privacy Analytics, pp. 55–63. ACM (2017)
Vu, L., Nguyen, P., Turaga, D.: Firstfilter: a cost-sensitive approach to malicious URL detection in large-scale enterprise networks. IBM J. Res. Dev. 60(4), 4:1–4:10 (2016)
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Patgiri, R., Katari, H., Kumar, R., Sharma, D. (2019). Empirical Study on Malicious URL Detection Using Machine Learning. In: Fahrnberger, G., Gopinathan, S., Parida, L. (eds) Distributed Computing and Internet Technology. ICDCIT 2019. Lecture Notes in Computer Science(), vol 11319. Springer, Cham. https://doi.org/10.1007/978-3-030-05366-6_31
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DOI: https://doi.org/10.1007/978-3-030-05366-6_31
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