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http://dx.doi.org/10.13089/JKIISC.2021.31.2.211

HTTP Intrusion Detection Model Using Machine Learning in Hadoop Environment  

Kim, Hyung-gi (Graduate School of Information & Communications SungKyunKwan University)
Kim, Moon-hyun (Graduate School of Information & Communications SungKyunKwan University)
Publication Information
Journal of the Korea Institute of Information Security & Cryptology / v.31, no.2, 2021 , pp. 211-220 More about this Journal
Abstract
Since the mobile era, Internet traffic usage has been increasing exponentially, and through this, various intrusion accidents and abnormal traffic are increasing rapidly. Recently, infringement accidents are occurring in a more diversified, intelligent and complex form, and various methods other than the existing methods are required to detect them. Therefore, in this study, a method to detect web service intrusion attempts by collecting HTTP service traffic as big data is implemented and verified using SVM and decision tree, which is a machine learning-based supervised learning without using patterns. In order to compensate for the limitations, I would like to study how to apply the Word to Vector method based on unsupervised learning.
Keywords
Machine Learning; Word to Vector; SVM; Hadoop;
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