Feature Subset Selection Hybrid Deep Belief Network Based Cybersecurity Intrusion Detection Model

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%.

Automated summary

Intrusion detection system (IDS) is crucial for network security, and designing an effective IDS requires identifying essential features and preprocessing network traffic data. This paper proposes a Feature Subset Selection Hybrid Deep Belief Network based Cybersecurity Intrusion Detection (FSHDBN-CID) model. The FSHDBN-CID model focuses on intrusion recognition for network security, with different levels of data preprocessing and feature selection using the jaya optimization algorithm (JOA). It utilizes the HDBN model for classification, and can be enhanced with the chicken swarm optimization (CSO) technique. Experimental results show that the FSHDBN-CID model outperforms other models with an accuracy of 99.57%.