Along with expansion in using of Internet and computer networks, the privacy, integrity, and access to digital resources have been faced with permanent risks. Due to the unpredictable behavior of network, the nonlinear nature of intrusion attempts, and the vast number of features in the problem environment, intrusion detection system (IDS) is regarded as the main problem in the security of computer networks. A feature selection technique helps to reduce complexity in terms of both the executive load and the storage by selecting the optimal subset of features. The purpose of this study is to identify important and key features in building an IDS. To improve the performance of IDS, this paper proposes an IDS that its features are optimally selected using a new hybrid method based on fruit fly algorithm (FFA) and ant lion optimizer (ALO) algorithm. The simulation results on the dataset KDD Cup99, NSL‐KDD, and UNSW‐NB15 have shown that the FFA–ALO has an acceptable performance according to the evaluation criteria such as accuracy and sensitivity than previous approaches.

中文翻译：

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基于包装的功能选择，可提高入侵检测系统的性能

随着Internet和计算机网络使用的扩展，隐私，完整性和对数字资源的访问面临永久性风险。由于网络行为的不可预测性，入侵尝试的非线性性质以及问题环境中的众多功能，入侵检测系统（IDS）被视为计算机网络安全中的主要问题。特征选择技术通过选择特征的最佳子集，有助于降低执行负载和存储方面的复杂性。本研究的目的是确定构建IDS的重要和关键功能。为了提高入侵检测系统的性能，本文提出了一种基于果蝇算法（FFA）和蚁群优化器（ALO）算法的混合算法，对入侵检测系统的特征进行了最优选择。