Intrusion detection is one of the most crucial activities for security infrastructures in network environments, and it is widely used to detect, identify and track malicious threats. A common approach in intrusion detection systems (IDSs) specifically in anomaly detection is evolutionary algorithm that works as intrusion detector. Still, it has been challenging to design a precise and reliable IDS to determine security threats due to the large capacity of network data which contains redundant and irrelevant features. It does not only decrease the process of classification but also prevents a classifier from making precise decisions. To increase the accuracy and reduce the false alarm rate, in this study integration of ensemble feature selection (EFS) and grasshopper optimization algorithm (GOA), called EFSGOA is developed. Firstly, EFS method is applied to rank the features for selecting the top subset of relevant features. Afterward, GOA is utilized to identify significant features from the obtained reduced features set produced by EFS technique that can contribute to determine the type of attack. Furthermore, GOA utilizes support vector machine (SVM) as a fitness function to obtain the noteworthy features and to optimize penalty factor, kernel parameter, and tube size parameters of SVM for maximizing the classification performance. The experimental results demonstrate that EFSGOA method has performed better and obtained high detection rate of 99.69%, accuracy of 99.98% and low false alarm rate of 0.07 in NSL-KDD and high detection rate of 99.26%, accuracy of 99.89% and low false alarm rate of 0.097 in KDD Cup 99 data. Moreover, the proposed method has succeeded in achieving higher performance compared to other state-of-art techniques in terms of accuracy, detection rate, false alarm rate, and CPU time.

入侵检测是网络环境中安全基础结构最重要的活动之一，它被广泛用于检测，识别和跟踪恶意威胁。专门用于异常检测的入侵检测系统（IDS）中的常见方法是用作入侵检测器的进化算法。但是，由于包含冗余和不相关功能的网络数据的大容量，设计精确可靠的IDS来确定安全威胁仍然具有挑战性。它不仅减少了分类的过程，而且阻止了分类器做出精确的决策。为了提高准确性并降低误报率，本研究开发了集成特征选择（EFS）和蚱hopper优化算法（GOA）的集成，称为EFSGOA。首先，EFS方法用于对特征进行排名，以选择相关特征的顶部子集。之后，GOA被用于从EFS技术产生的减少的特征集中识别重要特征，这些特征可以有助于确定攻击的类型。此外，GOA利用支持向量机（SVM）作为适应度函数来获取值得注意的特征，并优化SVM的惩罚因子，内核参数和管尺寸参数，以最大化分类性能。实验结果表明，EFSGOA方法在NSL-KDD中的检测率较高，检出率高达99.69％，准确率99.98％，误报率低至0.07，检出率高达99.26％，准确率99.89％，误报率低。 KDD Cup 99数据中的速率为0.097。此外，