Deep Learning-Based Anomaly Detection using auto-encoders for IoT  Systems with Blockchain-Enabled Trust Mechanisms: Enhancing Security  and Data Integrity

Sruthi Vaddelli  ,V.Mary Anita Rajam

Deep Learning-Based Anomaly Detection using auto-encoders for IoT Systems with Blockchain-Enabled Trust Mechanisms: Enhancing Security and Data Integrity

Authors

Sruthi Vaddelli ,V.Mary Anita Rajam

Keywords:

Abstract

This research focuses on developing an integrated framework for anomaly detection inInternet of Things (IoT) systems by leveraging deep learning techniques to detect anomalousbehaviors and utilizing blockchain technology to ensure the security, trust, and integrity of the system. IoT systems are highly vulnerable to cyber-attacks

References

Al-Garadi, M. A., et al. (2023). Deep Learning using LSTM for anomaly detection in IoT systems. Journal of Computer Networks and Communications, 2023.

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Published

2024-03-20

How to Cite

Sruthi Vaddelli ,V.Mary Anita Rajam. (2024). Deep Learning-Based Anomaly Detection using auto-encoders for IoT Systems with Blockchain-Enabled Trust Mechanisms: Enhancing Security and Data Integrity . Journal of Computational Analysis and Applications (JoCAAA), 33(2), 2283–2292. Retrieved from https://www.eudoxuspress.com/index.php/pub/article/view/5019