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Attention-based ResNet for intrusion detection and severity analysis using sliding window blockchain and firewall in IoT

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Abstract

With the increasing adoption of Internet of Things (IoT) networks, ensuring their security has become a critical concern due to resource limitations and the growing complexity of malicious attacks. Intrusion Detection and Prevention Systems play a pivotal role in safeguarding network performance, but traditional methods often struggle with attack severity and classifying unknown packets. In this research, we introduce the Attention-IDS model, a comprehensive solution comprising five stages: two-fold authentication, local density-based clustering, flow-based feature extraction, intrusion detection system (IDS), and intrusion severity detection. Leveraging IoT devices and user-based authentication, our model effectively detects and prevents unauthorized access attempts, while ensuring enhanced security through the utilization of the Combine Counter Mode algorithm on the blockchain. The IDS stage, powered by the Isolation Forest algorithm, accurately classifies features as normal, malicious, or unknown. Leveraging the proposed Attention-based ResNet model, our approach intelligently classifies unknown packets into normal and malicious categories, employing feature extraction, selection, and classification. Additionally, the Extended Kalman Filter determines intrusion severity, enabling network-wide notification alarms for frequent intrusions and targeted responses for rare intrusions. Extensive simulations using the NS3.26 network simulator demonstrate the superior performance of Attention-IDS compared to existing methods.

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Acknowledgements

This research work was supported by the National Natural Science Foundation of China with No. 62027826 and 61902052, “Science and Technology Major Industrial Project of Liaoning Province” with No. 2020JH1/10100013, “Dalian Science and Technology Innovation Fund” with No. 2020JJ26GX037, and also by: The Ministry of Science and Technology, Taiwan, R.O.C. under Grant MOST 110-2221-E-182-041-MY3.

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Authors and Affiliations

  1. Dalian University of Technology, Dalian, China

    Ernest Ntizikira, Lei Wang & Xinxin Lu

  2. Department of Computer Science and Information Engineering, Chang Gung University, Guishan Dist., 33302, Taoyuan City, Taiwan

    Jenhui Chen

  3. Department of Surgery, Div. of Breast Surgery and General Surgery, Chang Gung Memorial Hospital, Guishan Dist., 33375, Taoyuan City, Taiwan

    Jenhui Chen

  4. Department of Electronic Engineering, Ming Chi University of Technology, Taishan Dist., 24301, New Taipei City, Taiwan

    Jenhui Chen

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  1. Ernest Ntizikira
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  2. Lei Wang
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  4. Xinxin Lu
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Contributions

All authors contributed to the study’s conception and design. EN performed material preparation, data collection, the first draft of manuscript writing, and data analysis. The first draft of the manuscript was verified and commented on by XL. Its improvement, supervision, and funding acquisition have been managed by WL and JC. Finally, all authors read and approved the final manuscript.

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Correspondence to Ernest Ntizikira.

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Ntizikira, E., Wang, L., Chen, J. et al. Attention-based ResNet for intrusion detection and severity analysis using sliding window blockchain and firewall in IoT. Cluster Comput (2024). https://doi.org/10.1007/s10586-024-04310-z

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