Cyber Security Intrusion Detection Based on Deep Learning

ABSTRACT The rapid growth of heterogeneous network environments such as the Internet of Things (IoT), Industrial IoT (IIoT), cloud computing, and software‐defined networks has significantly increased exposure to sophisticated cyberattacks, making intrusion detection a critical component of modern cybersecurity infrastructures. Traditional intrusion detection systems and conventional machine learning techniques often face limitations when handling high‐dimensional network traffic, class imbalance, and evolving attack patterns, resulting in reduced detection performance and limited scalability under complex network environments. These challenges reduce their effectiveness in practical, large‐scale deployments. To overcome these issues, this paper proposes a hybrid intrusion detection framework based on an Autoencoder and a TabTransformer, optimized using the Whale Optimization Algorithm (WOA). The Autoencoder is employed to perform unsupervised feature learning, transforming high‐dimensional network traffic data into compact and noise‐resistant latent representations. These latent features are then processed by the TabTransformer, which utilizes multi‐head self‐attention to capture complex inter‐feature relationships and enhance classification performance. The WOA is incorporated to automatically optimize key hyperparameters, improving convergence speed, stability, and generalization capability of the model. The proposed framework is primarily evaluated using the CIC‐IDS2018 benchmark dataset. In addition, supplementary cross‐dataset validation on the CIC‐IDS2017 and UNSW‐NB15 datasets is conducted to assess the generalization capability of the proposed framework. Experimental results demonstrate that the proposed model achieves an accuracy of 99.87%, precision of 99.85%, recall of 99.88%, and an F1‐score of 99.86% while maintaining very low false alarm and false negative rates. Comparative analysis with existing deep learning‐based intrusion detection approaches confirms the superior and balanced performance of the proposed method. Overall, the Hybrid Autoencoder–TabTransformer framework provides an effective intrusion detection solution that demonstrates strong performance under the evaluated experimental conditions.