Novel Approach for Network Intrusion Detection using Machine Learning

Sachi Pandey
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Abstract

Advancement in the technologies has led to increment in the massive amount of data that massive amount of generated data has to be secured in such way that third party should not be able to take control over them The online platforms such as face book which has large number of users are the main sources of generating large amount of data ,each users activity on the internet is being captured in one or the other ways, security of network has become a great challenge in this modern era. Hence it has become very important to build an effective intrusion detection system. We have implemented a compressive survey of some of the major machine learning techniques based on Naïve Bays Classifier, K Nearest Neighbors Classifier, Decision Tree Classifier and the Logistic Regression in this paper.
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基于机器学习的网络入侵检测新方法
技术的进步导致了大量的增量数据,生成大量的数据必须是第三方担保等方式不应该能够控制他们的在线平台,如脸书的主要来源是大量的用户生成大量的数据,每个用户活动在互联网上被捕获在一个或另一个方面,网络安全已经成为一个巨大的挑战在这个现代。因此,建立一个有效的入侵检测系统就显得尤为重要。在本文中,我们对基于Naïve bayes分类器、K近邻分类器、决策树分类器和逻辑回归的一些主要机器学习技术进行了压缩调查。
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