Network Intrusion Detection Systems (NIDSs) are important in safeguarding networks from known and unknown attacks. Many research efforts have recently been made to create NIDS systems based on Machine Learning (ML) methods, addressing a significant challenge in designing standard NIDS the lack of standardized feature sets in the dataset. Given the recent development of the Internet of Things (IoT) in wireless communication, our proposed method introduces a novel solution to enhance intrusion detection systems. This proposed solution feature selection is carried out in two stages, coarse and fine selection. In the first stage of the coarse selection process, we conduct correlation analysis to identify relationships within the feature set. The second stage employs fine selection using the Whale Optimization Algorithm (WOA) with Genetic Algorithm hybridization (CFWOAGA). The fitness of each selected feature is assessed using the K‐Nearest Neighbors (KNN) algorithm. In our proposed work we integrate WOA with hybrid GA to extend the search space and avoid local optima problems via crossover and mutation operations. These selected features are critical for detecting any intrusion, we use an ML classifier to identify whether there is an attack or normal in the network and we evaluate the performance of each classifier. We evaluate the performance of our classifier using the BoT‐IoT 2020 standard dataset while limiting the selected features to 32 for reduced computational complexity, these selected 32 features are based upon considerations of system optimization and efficiency, making a balance between computational efficiency and model performance. The experimental findings show better model accuracy compared to the WOA technique and a significant drop in the False Alarm Rate (FAR). In conclusion, our proposed CFWOA method achieved an accuracy of 98.9%, while an updated version with the genetic algorithm demonstrated further improvement at 99.5%. Notably, there was a substantial improvement in FAR with our proposed method.

中文翻译：

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物联网网络中网络入侵检测系统 (IDS) 的粗略和精细特征选择

网络入侵检测系统 (NIDS) 对于保护网络免受已知和未知攻击非常重要。最近，人们进行了许多研究工作来创建基于机器学习 (ML) 方法的 NIDS 系统，解决了设计标准 NIDS 时数据集中缺乏标准化特征集的重大挑战。鉴于无线通信领域物联网 (IoT) 的最新发展，我们提出的方法引入了一种增强入侵检测系统的新颖解决方案。该解决方案特征选择分两个阶段进行：粗选和精选。在粗选过程的第一阶段，我们进行相关性分析以识别特征集中的关系。第二阶段采用鲸鱼优化算法（WOA）和遗传算法杂交（CFWOAGA）的精细选择。使用 K 最近邻 (KNN) 算法评估每个选定特征的适合度。在我们提出的工作中，我们将 WOA 与混合 GA 相结合，以扩展搜索空间并通过交叉和变异操作避免局部最优问题。这些选定的特征对于检测任何入侵至关重要，我们使用 ML 分类器来识别网络中是否存在攻击或正常，并评估每个分类器的性能。我们使用 BoT‐IoT 2020 标准数据集评估分类器的性能，同时将所选特征限制为 32 个以降低计算复杂度，这 32 个特征的选择是基于系统优化和效率的考虑，在计算效率和模型性能之间取得平衡。实验结果表明，与 WOA 技术相比，模型精度更高，误报率 (FAR) 显着下降。总之，我们提出的 CFWOA 方法的准确率达到了 98.9%，而遗传算法的更新版本则进一步提高了 99.5%。值得注意的是，我们提出的方法在 FAR 方面有了显着的改进。