Due to the rapid growth of internet services, the demand for protection and security of the network against sophisticated attacks is continuously increasing. Nowadays, in network security, an intrusion detection system (IDS) plays an important role to detect intrusive activity. With the purpose of reducing the search dimensionality and enhancing classification performance of IDS model, in the literature several hybrid evolutionary algorithms have been investigated to tackle anomaly detection problems, but they have few drawbacks such as poor diversity, massive false negative rate, and stagnation. To resolve these limitations, in this study, we introduce a new hybrid evolutionary algorithm combining the techniques of grasshopper optimization algorithm (GOA) and simulated annealing (SA), called GOSA for IDS that extracts the most noteworthy features and eliminates irrelevant ones from the original IDS datasets. In the proposed method, SA is integrated into GOA, while utilizing it to increase the solution quality after each iteration of GOA. Support vector machine is used as a fitness function in the proposed method to select relevant features which can help to classify attacks accurately. The performance of the proposed method is evaluated on two IDS datasets such as NSL-KDD and UNSW-NB15. From experimental results, we observe that the proposed method outperforms existing state-of-the-art methods and attains high detection rate as 99.86%, an accuracy as 99.89%, and low false alarm rate as 0.009 in NSL-KDD and high detection rate as 98.85%, an accuracy as 98.96%, and low false alarm rate as 0.084 in UNSW-NB15.

中文翻译：

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结合进化计算来保护无线网络免受网络威胁

由于互联网服务的快速增长，对网络保护和安全防范复杂攻击的需求不断增加。如今，在网络安全中，入侵检测系统（IDS）在检测入侵活动方面发挥着重要作用。为了降低IDS模型的搜索维数和提高分类性能，文献中研究了几种混合进化算法来解决异常检测问题，但它们几乎没有多样性差、假阴性率高和停滞等缺点。为了解决这些限制，在本研究中，我们引入了一种新的混合进化算法，结合了蚱蜢优化算法（GOA）和模拟退火（SA）的技术，称为 GOSA for IDS 提取最值得注意的特征并从原始 IDS 数据集中消除不相关的特征。在所提出的方法中，SA 被集成到 GOA 中，同时利用它来提高 GOA 每次迭代后的解决方案质量。在所提出的方法中使用支持向量机作为适应度函数来选择有助于准确分类攻击的相关特征。在 NSL-KDD 和 UNSW-NB15 等两个 IDS 数据集上评估了所提出方法的性能。从实验结果，我们观察到所提出的方法优于现有的最先进的方法，并且在 NSL-KDD 中达到了 99.86% 的高检测率，99.89% 的准确率和 0.009 的低误报率和高检测率在 UNSW-NB15 中为 98.85%，准确率为 98.96%，低误报率为 0.084。