Predicting IoT Botnet Attacks for Enhanced Data Transmission Security in the Cloud Using Variational Autoencoders

Authors

  • Chandrasekar Venkatachalam Professor, Department of CSE, Faculty of Engineering and Technology, Jain (Deemed-to-be) University, Bangalore, Karnataka
  • P. Selvaraju Associate Professor, Department of Computer Science and Engineering, Excel Engineering College, Namakkal, Tamilnadu
  • Tamil Selvan Sivalingam Assistant Professor & Head, Department of Computer Science and Design, Erode Sengunthar Engineering College, Thudupathi, Perundurai, Erode – 638057
  • V. Rajakumareswaran Assistant Professor, Dept of Computer Science and Design, Erode Sengunthar Engineering College, Erode - Tamilnadu, India, 638057
  • K. Mohanambal Assistant Professor l (Level l), Bannariamman Institute of Technology, Sathyamangalam, Tamilnadu, 638401
  • M. Murali Assistant Professor, Department of IT, Sona College of Technology, Salem, Tamilnadu, 636005

Keywords:

Data Transmission, Security Threat Prediction, Variational Autoencoders (VAEs), IoT Security, Cloud Computing, IoT Botnet Attacks

Abstract

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.

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Published

24.03.2024

How to Cite

Venkatachalam, C. ., Selvaraju, P. ., Sivalingam, T. S. ., Rajakumareswaran, V. ., Mohanambal, K., & Murali, M. (2024). Predicting IoT Botnet Attacks for Enhanced Data Transmission Security in the Cloud Using Variational Autoencoders. International Journal of Intelligent Systems and Applications in Engineering, 12(20s), 820–828. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/5307

Issue

Vol. 12 No. 20s (2024)

Section

Research Article

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