论文标题
网络攻击检测算法选择框架
Algorithm Selection Framework for Cyber Attack Detection
论文作者
论文摘要
每年对有线和无线计算机系统以及其他组件的网络威胁数量每年不断增加。在这项工作中，NSL-KDD数据集采用了算法选择框架，并提出了机器学习分类法的新型范式。该框架结合了用户输入和元功能的组合来选择最佳的算法来检测网络上的网络攻击。比较跑步规则策略和元学习策略之间的绩效。该框架消除了常见的试用算法选择方法的猜想。该框架建议使用分类法的五种算法。两种策略都建议使用高性能的算法，尽管不是表现最好的算法。这项工作表明了算法选择与前提的分类法之间的紧密联系。
The number of cyber threats against both wired and wireless computer systems and other components of the Internet of Things continues to increase annually. In this work, an algorithm selection framework is employed on the NSL-KDD data set and a novel paradigm of machine learning taxonomy is presented. The framework uses a combination of user input and meta-features to select the best algorithm to detect cyber attacks on a network. Performance is compared between a rule-of-thumb strategy and a meta-learning strategy. The framework removes the conjecture of the common trial-and-error algorithm selection method. The framework recommends five algorithms from the taxonomy. Both strategies recommend a high-performing algorithm, though not the best performing. The work demonstrates the close connectedness between algorithm selection and the taxonomy for which it is premised.
