K. S. Sarin, R. E. Kolomnikov, M. O. Svetlakov, I. A. Hodashinsky
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引用次数: 0
Abstract
A modified fuzzy min-max classifier is presented that differs from the original in the way that the hyperbox expansion operation is performed. The classifier has been tested on the solution of cybersecurity problems, such as detecting spam, phishing sites and attacks on network connections. The results of experiments results showed an improvement in the accuracy relative to the original fuzzy min-max classifier. Comparisons with six alternative incremental learning classifiers showed competitive results on the false acceptance rate, the false reject rate, and the F1-score values.
本文介绍了一种改进的模糊最小最大分类器，它与原始分类器的不同之处在于执行超箱扩展操作的方式。该分类器已在解决网络安全问题（如检测垃圾邮件、钓鱼网站和网络连接攻击）方面进行了测试。实验结果表明，相对于原始的模糊最小最大分类器，该分类器的准确率有所提高。与其他六种增量学习分类器的比较结果显示，在错误接受率、错误拒绝率和 F1 分数值方面，该分类器都具有竞争力。
期刊介绍：
Automatic Documentation and Mathematical Linguistics is an international peer reviewed journal that covers all aspects of automation of information processes and systems, as well as algorithms and methods for automatic language analysis. Emphasis is on the practical applications of new technologies and techniques for information analysis and processing.
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