Securing IoT Edge Devices through Optimized Deep Learning Models a  Multi-Objective Approach

Naveen Sai Bommina , Nandipati Sai Akash, Uppu Lokesh , Dr. Hussain Syed, Dr. Syed  Umar

Securing IoT Edge Devices through Optimized Deep Learning Models a Multi-Objective Approach

Authors

Naveen Sai Bommina , Nandipati Sai Akash, Uppu Lokesh , Dr. Hussain Syed, Dr. Syed Umar

Keywords:

IoT Security, Edge Devices, Deep Learning, Multi-Objective Optimization, Anomaly Detection, Lightweight Neural Networks, Model Pruning, Quantization, Neural Architecture Search (NAS), Cyberattack Detection, Real-Time Security

Abstract

The proliferation of Internet of Things (IoT) edge devices in critical infrastructure and smartenvironments has significantly increased the surface area for cyber-attacks, necessitating robust,adaptive, and lightweight security mechanisms.

References

Diro, A. A., & Chilamkurti, N. (2018). Distributed attack detection scheme using deep learning approach for IoT. Future Generation Computer Systems, 82, 761–768.

Yin, C., Zhu, Y., Fei, J., & He, X. (2017). A deep learning approach for intrusion detection using recurrent neural networks. IEEE Access, 5, 21954–21961.

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Published

2019-10-21

How to Cite

Naveen Sai Bommina , Nandipati Sai Akash, Uppu Lokesh , Dr. Hussain Syed, Dr. Syed Umar. (2019). Securing IoT Edge Devices through Optimized Deep Learning Models a Multi-Objective Approach . Journal of Computational Analysis and Applications (JoCAAA), 27(7), 58–70. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/3102