A Comparative Evaluation of Intrusion Detection Systems: ICVAE vs. CVAE

In the domain of network security, Intrusion Detection Systems (IDS) are indispensable guardians against cyber threats. However, conventional IDS approaches often grapple with the complexities posed by unknown attacks and uneven data distributions. This study undertakes a comparative examination of two advanced IDS models: a Conditional Variational Autoencoder (CVAE) and an Integrated Conditional Variational Autoencoder with a Deep Neural Network (ICVAE-DNN). The CVAE model embodies a refined methodology leveraging variational autoencoder techniques to discern the underlying patterns within network data. Yet, it encounters challenges in effectively handling imbalanced datasets and unidentified threats. Conversely, the ICVAE-DNN model builds upon the CVAE framework by incorporating a deep neural network, facilitating improved data distribution balancing and heightened detection capabilities through the generation of novel attack samples. Moreover, the inclusion of ICVAE empowers the fine-tuning of the DNN, enhancing overall performance. Evaluation of the NSL-KDD dataset underscores ICVAE-DNN's supremacy over conventional approaches and CVAE in identifying minority and unknown attacks. Additionally, this research pioneers an IDS tailored specifically for IoT networks, harnessing a conditional variational autoencoder. This innovative approach, integrating intrusion labels, demonstrates promising outcomes in classification and feature reconstruction, crucial facets of IoT security. In conclusion, this study propels intrusion detection methodologies forward by demonstrating ICVAE-DNN's superiority over CVAE. By surmounting the limitations of traditional techniques and embracing novel methodologies, this research contributes significantly to the ongoing battle against emerging threats in network security.