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Temporal Gan Ensemble with Bagging for Robust Information Security in Iot Sensor Networks


Affiliations
1 Department of Computer Science and Engineering, Anand Institute of Higher Technology, India
2 Department of Artificial Intelligence and Data Science, KGiSL Institute of Technology, India
3 Department of Electronics and Communication Engineering, Dhanekula Institute of Engineering and Technology, India
4 Department of Electrical and Electronics Engineering, School of Engineering and Technology, Dhanalakshmi Srinivasan University, India
     

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In the ever-evolving landscape of IoT sensor networks, ensuring robust information security is imperative. This paper introduces a novel approach, the Temporal GAN Ensemble with Bagging (TGE-Bag), designed to fortify the security framework of IoT sensor networks. TGE-Bag leverages the power of Generative Adversarial Networks (GANs) with a temporal dimension, addressing the dynamic nature of IoT data streams. The ensemble aspect incorporates Bagging, enhancing the overall resilience and robustness of the security model. The temporal dimension in TGE-Bag recognizes the time-sensitive nature of IoT data, acknowledging that threats and anomalies may manifest differently over time. By incorporating GANs, the model can effectively generate synthetic data representative of the temporal patterns, allowing for more comprehensive training and robust anomaly detection. The ensemble approach further contributes to the model robustness by aggregating diverse GANs, each specialized in capturing specific temporal nuances. This paper evaluates TGE-Bag efficacy through extensive simulations on real-world IoT datasets, demonstrating its superior performance in detecting and mitigating security threats. The ensemble ability to generalize across diverse temporal patterns contributes to its adaptability in various IoT sensor network scenarios.

Keywords

Temporal GAN, Ensemble Learning, Bagging, IoT Security, Anomaly Detection.
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  • Temporal Gan Ensemble with Bagging for Robust Information Security in Iot Sensor Networks

Abstract Views: 134  |  PDF Views: 1

Authors

S. Roselin Mary
Department of Computer Science and Engineering, Anand Institute of Higher Technology, India
K. Selva Sheela
Department of Artificial Intelligence and Data Science, KGiSL Institute of Technology, India
Srinivasa Rao Kandula
Department of Electronics and Communication Engineering, Dhanekula Institute of Engineering and Technology, India
R. Ramkumar
Department of Electrical and Electronics Engineering, School of Engineering and Technology, Dhanalakshmi Srinivasan University, India

Abstract


In the ever-evolving landscape of IoT sensor networks, ensuring robust information security is imperative. This paper introduces a novel approach, the Temporal GAN Ensemble with Bagging (TGE-Bag), designed to fortify the security framework of IoT sensor networks. TGE-Bag leverages the power of Generative Adversarial Networks (GANs) with a temporal dimension, addressing the dynamic nature of IoT data streams. The ensemble aspect incorporates Bagging, enhancing the overall resilience and robustness of the security model. The temporal dimension in TGE-Bag recognizes the time-sensitive nature of IoT data, acknowledging that threats and anomalies may manifest differently over time. By incorporating GANs, the model can effectively generate synthetic data representative of the temporal patterns, allowing for more comprehensive training and robust anomaly detection. The ensemble approach further contributes to the model robustness by aggregating diverse GANs, each specialized in capturing specific temporal nuances. This paper evaluates TGE-Bag efficacy through extensive simulations on real-world IoT datasets, demonstrating its superior performance in detecting and mitigating security threats. The ensemble ability to generalize across diverse temporal patterns contributes to its adaptability in various IoT sensor network scenarios.

Keywords


Temporal GAN, Ensemble Learning, Bagging, IoT Security, Anomaly Detection.

References