Intrusion detection system (IDS) has played a significant role in modern network security. A key component for constructing an effective IDS is the identification of essential features and network traffic data preprocessing to design effective classification model. This paper presents a Feature Subset Selection Hybrid Deep Belief Network based Cybersecurity Intrusion Detection (FSHDBN-CID) model. The presented FSHDBN-CID model mainly concentrates on the recognition of intrusions to accomplish cybersecurity in the network. In the presented FSHDBN-CID model, different levels of data preprocessing can be performed to transform the raw data into compatible format. For feature selection purposes, jaya optimization algorithm (JOA) is utilized which in turn reduces the computation complexity. In addition, the presented FSHDBN-CID model exploits HDBN model for classification purposes. At last, chicken swarm optimization (CSO) technique can be implemented as a hyperparameter optimizer for the HDBN method. In order to investigate the enhanced performance of the presented FSHDBN-CID method, a wide range of experiments was performed. The comparative study pointed out the improvements of the FSHDBN-CID model over other models with an accuracy of 99.57%.

中文翻译：

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基于特征子集选择的混合深度信任网络网络安全入侵检测模型

入侵检测系统（IDS）在现代网络安全中发挥了重要作用。构建有效 IDS 的一个关键组成部分是识别基本特征和网络流量数据预处理以设计有效的分类模型。本文提出了一种基于特征子集选择混合深度信念网络的网络安全入侵检测 (FSHDBN-CID) 模型。提出的 FSHDBN-CID 模型主要集中在识别入侵以实现网络中的网络安全。在提出的 FSHDBN-CID 模型中，可以执行不同级别的数据预处理以将原始数据转换为兼容格式。出于特征选择的目的，使用了jaya优化算法（JOA），这反过来又降低了计算复杂度。此外，提出的 FSHDBN-CID 模型利用 HDBN 模型进行分类。最后，鸡群优化（CSO）技术可以作为 HDBN 方法的超参数优化器来实现。为了研究所提出的 FSHDBN-CID 方法的增强性能，进行了广泛的实验。对比研究指出 FSHDBN-CID 模型相对于其他模型的改进，准确率达到 99.57%。