One of the important issues in the computer networks is security. Therefore, trusted communication of information in computer networks is a critical point. To have a safe communication, it is necessary that, in addition to the prevention mechanisms, intrusion detection systems (IDSs) are used. There are various approaches to utilize intrusion detection, but any of these systems is not complete. In this paper, a new cross-correlation-based feature selection (CCFS) method is proposed and compared with the cuttlefish algorithm (CFA) and mutual information-based feature selection (MIFS) features with use of four different classifiers: support vector machine (SVM), naive Bayes (NB), decision tree (DT), and K-nearest neighbor (KNN). The experimental results on the KDD Cup 99, NSL-KDD, AWID, and CIC-IDS2017 datasets show that the proposed method has a better performance in accuracy, precision, recall, and F1-score criteria in comparison with the other two methods in different classifiers. Also, the results on different classifiers show that the usage of the DT classifier for the proposed method is the best.

中文翻译：

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基于互相关的入侵检测系统特征选择

安全是计算机网络中的重要问题之一。因此，计算机网络中信息的可信通信是关键。为了进行安全的通信，除了预防机制外，还必须使用入侵检测系统（IDS）。有多种利用入侵检测的方法，但是这些系统中的任何一个都不完整。本文提出了一种新的基于互相关的特征选择（CCFS）方法，并使用四个不同的分类器与墨鱼算法（CFA）和基于互信息的特征选择（MIFS）特征进行了比较：支持向量机（ SVM），朴素贝叶斯（NB），决策树（DT）和K最近邻居（KNN）。在KDD Cup 99，NSL-KDD，AWID，与不同分类器中的其他两种方法相比，F 1分标准。同样，在不同分类器上的结果表明，DT分类器在该方法中的使用效果最佳。