Intrusion Detection Systems (IDSs) is a system that monitors network traffic for suspicious activity and issues alert when such activity is revealed. Moreover, the existing IDSs-based methods are based on outdated attacks that unable to identify modern attacks or malicious trends. For this reason, in this study we developed a new multi-swarm adaptive grasshopper optimization algorithm to utilize adaptation mechanism in a group of swarms based on fuzzy logic to protect against sophisticated attacks. The proposed (MSAGOA) technique has the capability of global optimization and rapid convergence that are used to attain optimal feature subsets to identify attack types on IDS datasets. In the MSAGOA technique, learning engine as Extreme learning Machine, Naive Bayes, Random Forest and Decision Tree is applied as a fitness function to select the highly discriminating features and to maximize classification performance. Afterward, select the best classifier which works as a fitness function in our approach to measure the performance in terms of accuracy, detection rate, and false alarm rate. The simulations are performed on three IDS datasets such as NSL-KDD, AWID-ATK-R, and NGIDS-DS. The experimental results demonstrated that MSAGOA method has performed better and obtained high detection rate of 99.86%, accuracy of 99.89% in NSL-KDD and high detection rate of 98.73%, accuracy of 99.67% in AWID-ATK-R and detection rate of 89.50%, accuracy of 90.23% in NGIDS-DS. In addition, the performance is compared with several other existing techniques to show the efficacy of the proposed approach.

入侵检测系统（IDS）是一种监视网络流量中可疑活动并在发现此类活动时发出警报的系统。此外，现有的基于IDS的方法基于无法识别现代攻击或恶意趋势的过时攻击。因此，在这项研究中，我们开发了一种新的多群自适应蚱hopper优化算法，以利用基于模糊逻辑的一群群的自适应机制来防御复杂的攻击。所提出的（MSAGOA）技术具有全局优化和快速收敛的能力，可用于获得最佳特征子集以识别IDS数据集上的攻击类型。在MSAGOA技术中，作为极限学习机的学习引擎，朴素贝叶斯（Naive Bayes），随机森林和决策树被用作适应度函数，以选择高度区分的特征并最大化分类性能。然后，选择最佳分类器作为我们的方法的适应度函数，以准确性，检测率和误报率来衡量性能。对三个IDS数据集（例如NSL-KDD，AWID-ATK-R和NGIDS-DS）执行模拟。实验结果表明，MSAGOA方法具有较好的检测率，在NSL-KDD中检出率高达99.86％，准确率99.89％，在AWID-ATK-R中检出率高达98.73％，准确率达到99.67％，检出率89.50。 ％，在NGIDS-DS中的准确度为90.23％。此外，将性能与其他几种现有技术进行了比较，以证明所提出方法的有效性。