Network security is a mechanism of protecting the usability and integrity of any given network and its transmitted data. Network security's effectiveness is crucial to the network environment to ensure it is free from any threat, especially in the critical infrastructure (CI). The supervisory control and data acquisition systems in the CI are getting more connected to the internet, putting them in serious security concerns. Any malicious attack against these systems could cause considerable human, economic, and material damages. Thus, it leads to the emergence of the intrusion detection system (IDS). Theoretically, a modern IDS must handle a large amount of data with high accuracy. Ensemble-based, hybrid-based methods and their distinguished applications are a promising way to solve these issues. The efficiency of the IDS is mainly dependent on the selected data features and the used classification method. The artificial neural network (ANN) has been applied in various fields, but it requires adjustment on few parameters to work effectively. This study proposes a homogeneous ensemble based on single-class dynamic ANN (HOE-DANN). Each dynamic ANN (DANN) is optimized by a filter-wrapper method using a modified discrete cuttlefish algorithm based on rough set theory, and a migration-strategy based cuttlefish algorithm. Both algorithms simultaneously optimize the features, ANN structure, weights, and biases for creating the DANN. However, the threshold value of the ensemble model was set using the hill-climbing algorithm. The experiments were applied to well-known benchmark datasets, namely the KDD99, UNSW-NB15, and gas pipeline data logs (GPDL). The results show that the HOE-DANN outperforms the single model based on the DANN. Additionally, a comparison with several state-of-the-art methods has shown that the proposed method offers superior performance in terms of the detection rate (DR), false alarm rate (FAR), and classification accuracy (ACC). The HOE-DANN model was able to achieve DR of 97.47%, FAR of 2.25%, and ACC of 97.52% using the KDD99 dataset, DR of 99.93%, FAR of 13.13%, and ACC of 94.08% using the UNSW-NB15 dataset, and DR of 98.08%, FAR of 2.69%, and ACC of 94.50% using the GPDL dataset.

网络安全是一种保护任何给定网络及其传输数据的可用性和完整性的机制。网络安全的有效性对于确保网络环境免受任何威胁至关重要，尤其是在关键基础设施 (CI) 中。CI 中的监控和数据采集系统越来越多地连接到互联网，这使它们面临严重的安全问题。对这些系统的任何恶意攻击都可能造成相当大的人员、经济和物质损失。因此，它导致了入侵检测系统（IDS）的出现。理论上，现代 IDS 必须以高精度处理大量数据。基于集成、基于混合的方法及其卓越的应用是解决这些问题的一种很有前途的方法。IDS 的效率主要取决于选择的数据特征和使用的分类方法。人工神经网络 (ANN) 已应用于各个领域，但它需要对少数参数进行调整才能有效工作。本研究提出了一种基于单类动态人工神经网络（HOE-DANN）的同构集成。每个动态人工神经网络 (DANN) 都使用基于粗糙集理论的改进离散墨鱼算法和基于迁移策略的墨鱼算法通过过滤器包装方法进行优化。两种算法同时优化用于创建 DANN 的特征、ANN 结构、权重和偏差。然而，集成模型的阈值是使用爬山算法设置的。实验应用于著名的基准数据集，即 KDD99、UNSW-NB15、和天然气管道数据日志 (GPDL)。结果表明，HOE-DANN 优于基于 DANN 的单一模型。此外，与几种最先进的方法的比较表明，所提出的方法在检测率 (DR)、误报率 (FAR) 和分类精度 (ACC) 方面提供了卓越的性能。HOE-DANN 模型使用 KDD99 数据集能够实现 97.47% 的 DR、2.25% 的 FAR 和 97.52% 的 ACC，使用 UNSW-NB15 数据集的 DR 为 99.93%、FAR 为 13.13% 和 ACC 为 94.08% ，使用 GPDL 数据集，DR 为 98.08%，FAR 为 2.69%，ACC 为 94.50%。误报率 (FAR) 和分类准确率 (ACC)。HOE-DANN 模型使用 KDD99 数据集能够实现 97.47% 的 DR、2.25% 的 FAR 和 97.52% 的 ACC，使用 UNSW-NB15 数据集的 DR 为 99.93%、FAR 为 13.13% 和 ACC 为 94.08% ，使用 GPDL 数据集，DR 为 98.08%，FAR 为 2.69%，ACC 为 94.50%。误报率 (FAR) 和分类准确率 (ACC)。HOE-DANN 模型使用 KDD99 数据集能够实现 97.47% 的 DR、2.25% 的 FAR 和 97.52% 的 ACC，使用 UNSW-NB15 数据集的 DR 为 99.93%、FAR 为 13.13% 和 ACC 为 94.08% ，使用 GPDL 数据集，DR 为 98.08%，FAR 为 2.69%，ACC 为 94.50%。