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2017 IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC) (2017)
Guangzhou, China
July 21, 2017 to July 24, 2017
ISBN: 978-1-5386-3220-8
pp: 143-150
ABSTRACT
Web attacks are increasing and the scale of malicious URL continues to expand with the rapid development of the Internet, so that the network security situation is increasingly grim. In this case, this paper studies the URL multi-classification problem, which is a continuation of the reference [1] and follows the data sets and most of feature selection methods in it. Firstly, different types of URL structure is analyzed and features which have obvious directivity to the attack type are increased based on the original features. Secondly, an improved semi-supervised algorithm is proposed to train the URL multi-classification model. Finally, the effect of the multi-classification model is verified by using the data set with different labeling rate, which shows the improved algorithm has good classification performance compared to the previous algorithm.
INDEX TERMS
Uniform resource locators, Feature extraction, Training, Classification algorithms, Algorithm design and analysis, Malware, Security
CITATION

J. Yang, P. Yang, X. Jin and Q. Ma, "Multi-Classification for Malicious URL Based on Improved Semi-Supervised Algorithm," 2017 IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC)(CSE-EUC), Guangzhou, Guangdong, China, 2017, pp. 143-150.
doi:10.1109/CSE-EUC.2017.34
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