Detection of Intrusions with Machine Learning Methods

Beyzanur Bostanci, A. Albayrak
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Abstract

Today, especially with the emergence of social networks and IoT technologies, big data has entered the literature. With the development of technology, the size of the data has increased and accordingly data security gaps have emerged. In this study, Support Vector Machines and Random Forest algorithms, which are Supervised Machine Learning Algorithms, were used to analyze a data set consisting of unauthorized network logins. As a result of the experimental studies, it was observed that both algorithms produced good results, but the Random Forest approach produced better results.
用机器学习方法检测入侵
今天,特别是随着社交网络和物联网技术的出现,大数据已经进入了文献。随着技术的发展,数据的规模越来越大,相应的数据安全漏洞也出现了。在本研究中,使用支持向量机和随机森林算法(即监督机器学习算法)来分析由未经授权的网络登录组成的数据集。实验研究的结果表明,两种算法都产生了良好的结果,但随机森林方法产生了更好的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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