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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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  • M.E. Ahmed and H. Kim, “DDoS Attack Mitigation in Internet of Things Using Software Defined Networking”, Proceedings of International Conference on Big Data Computing Service and Applications, pp. 6-9, 2017.

  • L. Atzori and A. Iera, “The Internet of Things: A Survey”, Computer Networks, Vol. 54, No. 15, pp. 2787-2805, 2010.

  • P.K. Dhillon and S. Kalra, “Multi-Factor User Authentication Scheme for IoT-Based Healthcare Services”, Journal of Reliable Intelligent Environments, Vol. 4, No. 3, pp. 141-160, 2018.

  • Amiya Kumar, Suraj Sharma, Deepak Puthal, AbhishekPandey and Rathin Shit, “Secure Authentication Protocol for IoT Architecture”, Proceedings of International Conference on Information Technology, pp. 220-224, 2017.

  • J. Jiang and L. Shu, “Authentication protocols for Internet of Things: A Comprehensive Survey”, Security and Communication Networks, Vol. 2017, pp. 1-18, 2017.

  • T.K. Rodrigues and N. Kato, “Network Slicing with Centralized and Distributed Reinforcement Learning for Combined Satellite/Ground Networks in a 6G Environment”, IEEE Wireless Communications, Vol. 29, No. 1, pp. 104-110, 2022.

  • P. Gope and B. Sikdar, “Lightweight and Privacy-Preserving Two-Factor Authentication Scheme for IoT Devices”, IEEE Internet of Things, Vol. 6, No. 1, pp. 580-589, 2018.

  • D.S.K. Tiruvakadu and V. Pallapa, “Confirmation of Wormhole Attack in MANETs using Honeypot”, Computers and Security, Vol. 76, No. 2, pp. 32-49, 2018.

  • P.K. Dhillon and S. Kalra, “Multi-Factor User Authentication Scheme for IoT-Based Healthcare Services”, Journal of Reliable Intelligent Environments, Vol. 4, No. 3, pp. 141-160, 2018.

  • M. Elhoseny, K. Shankar and S.K. Lakshmanaprabu, “Hybrid Optimization with Cryptography Encryption for Medical Image Security in Internet of Things”, Neural Computing and Applications, Vol. 32, No. 15, pp. 1-15, 2018.

  • M. Zhou, L. Han, H. Lu and C. Fu, “Intrusion Detection System for IoT Heterogeneous Perceptual Network”, Mobile Networks and Applications, Vol. 33, No. 1, pp. 1-14, 2020.

  • A. Tabassum and W. Lebda, “Security Framework for IoT Devices against Cyber-Attacks”, Proceedings of International Conference on Internet of Things, pp. 1-18, 2019.

  • H. Sedjelmaci, S.M. Senouci and T. Taleb, “An Accurate Security Game for Low-Resource IoT Devices”, IEEE Transactions on Vehicular Technology, Vol. 66, No. 10, pp. 9381-9393, 2017.

  • F. Jiang, “Deep Learning based Multi-Channel Intelligent Attack Detection for Data Security”, IEEE Transactions on Sustainable Computing, pp. 1-10, 2018.

  • P. Kumar, G.P. Gupta and R. Tripathi, “An Ensemble Learning and Fog-Cloud Architecture-Driven Cyber-Attack Detection Framework for IoMT Networks”, Computer Communications, Vol. 166, pp. 110-124, 2021.


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

Abstract Views: 62  |  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