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HybridRobustNet: enhancing detection of hybrid attacks in IoT networks through advanced learning approach

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Abstract

The proliferation of Internet of Things (IoT) devices has revolutionized various domains, but it has also brought forth numerous security challenges. One of the most concerning threats is the emergence of hybrid attacks, which combine multiple attack vectors to exploit vulnerabilities in IoT networks. Existing security mechanisms often struggle to effectively predict and detect these sophisticated hybrid attacks, leading to compromised system integrity and data confidentiality. In this paper, we propose robust learning approach, named HybridRobustNet (HRN), for predicting and detecting hybrid attacks over IoT networks. HRN integrates machine learning algorithms, deep neural networks, and ensemble techniques to achieve enhanced detection accuracy and resilience against evolving hybrid attack patterns. By leveraging a diverse set of features, including network traffic patterns, device behavior, and communication characteristics, HRN effectively captures the complex relationships and dependencies between various attack components. Furthermore, the proposed approach incorporates real-time adaptive learning mechanisms, enabling it to dynamically adapt to new attack strategies and mitigate false positives. To evaluate the effectiveness of HRN, extensive experiments were conducted on a realistic IoT testbed comprising heterogeneous devices and attack scenarios. The results demonstrate that HRN outperforms state-of-the-art approaches in terms of attack detection accuracy, robustness against evasion techniques, and low false positive rates. Additionally, its computational efficiency makes it suitable for deployment in resource-constrained IoT environments. The contributions of this work are twofold. Firstly, it addresses the pressing need for robust detection mechanisms against hybrid attacks, which can have severe consequences for IoT networks. Secondly, it introduces a unique and adaptive learning approach, HRN, which exhibits superior performance and adaptability in the face of emerging attack strategies. The findings presented in this article provide valuable insights into the design of effective security mechanisms for IoT networks and pave the way for future research in the field of hybrid attack detection.

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Authors and Affiliations

  1. Department of Information Technology, School of Computing, Sathyabama Institute of Science and Technology, Chennai, Tamil Nadu, 600 119, India

    D. Adhimuga Sivasakthi & A. Sathiyaraj

  2. School of Computer Science and Engineering, VIT-AP University, Amaravati, Andhra Pradesh, 522 237, India

    Ramkumar Devendiran

Authors
  1. D. Adhimuga Sivasakthi
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  2. A. Sathiyaraj
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  3. Ramkumar Devendiran
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Contributions

Dr. D. Adhimuga Sivasakthi : Conceptualization, Methodology, and Project Administration.Dr. A Sathiyaraj: Data Collection, Pre-processing, and Model Implementation.Dr. Ramkumar Devendiran: Evaluation Metrics and Performance Analysis.

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Correspondence to Ramkumar Devendiran.

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Sivasakthi, D.A., Sathiyaraj, A. & Devendiran, R. HybridRobustNet: enhancing detection of hybrid attacks in IoT networks through advanced learning approach. Cluster Comput (2024). https://doi.org/10.1007/s10586-023-04248-8

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