论文标题
使用KNIME Analytics在网络安全中学习机器
Machine Learning in Network Security Using KNIME Analytics
论文作者
论文摘要
机器学习会对我们每天的生活产生越来越多的影响。该领域不断发展并扩展到新领域。机器学习是基于人工智能的实施，该实施使系统具有自动学习和增强实验的能力，而无需明确编程。机器学习算法应用数学方程式来分析数据集并基于数据集预测值。在网络安全领域，可以利用机器学习算法来训练和分析与安全相关数据集的入侵检测系统（IDSS）。在本文中，我们测试了不同的机器学习算法，以使用KNIME Analytics分析NSL-KDD数据集。
Machine learning has more and more effect on our every day's life. This field keeps growing and expanding into new areas. Machine learning is based on the implementation of artificial intelligence that gives systems the capability to automatically learn and enhance from experiments without being explicitly programmed. Machine Learning algorithms apply mathematical equations to analyze datasets and predict values based on the dataset. In the field of cybersecurity, machine learning algorithms can be utilized to train and analyze the Intrusion Detection Systems (IDSs) on security-related datasets. In this paper, we tested different machine learning algorithms to analyze NSL-KDD dataset using KNIME analytics.
