Predicting IoT Botnet Attacks for Enhanced Data Transmission Security in the Cloud Using Variational Autoencoders - Venkatachalam, Chandrasekar; Selvaraju, P.; Sivalingam, Tamil Selvan; Rajakumareswaran, V.; Mohanambal, K.; Murali, M. (International Journal of Intelligent Systems and Applications in Engineering, 2024)
In the expansive domain of the Internet of Things (IoT), ensuring the integrity of data transmission within cloud computing systems takes precedence as a critical concern. This research focuses on proactively identifying and mitigating IoT security threats, particularly botnet attacks, using advanced Variational Autoencoders (VAEs) with the UNSW-NB15 dataset. The model achieved an impressive accuracy of 99.74%, highlighting its effectiveness in predicting IoT botnet attacks. Through rigorous evaluation and comparative analysis, the study establishes the superiority of the VAE-based model. Beyond immediate applications, the model has transformative potential for enhancing data transmission security in IoT and cloud computing. This research paves the way for groundbreaking advancements, envisioning a future where information flows securely in the interconnected global landscape, ensuring a safer and more resilient digital environment in the era of IoT.