| Issue |
MATEC Web Conf.
Volume 392, 2024
International Conference on Multidisciplinary Research and Sustainable Development (ICMED 2024)
|
|
|---|---|---|
| Article Number | 01141 | |
| Number of page(s) | 8 | |
| DOI | https://doi.org/10.1051/matecconf/202439201141 | |
| Published online | 18 March 2024 | |
Blockchain-enabled collaborative anomaly detection for IoT security
1 Intel Corporation, Hillsboro, Oregon 97124, USA
2 Department of Emerging Technologies, Guru Nanak Institute of Technology, Ibrahimpatnam, Telangana, India, 501506
3 Department of ECE, Hyderabad Institute of Technology and Management, India
4 Department of EEE, Institute of Aeronautical Engineering, Hyderabad, India
5 Rajeev Institute of Technology, Hassan, India
6 Department of Computer Science & Engineering, Koneru Lakshmaiah Education Foundation, Green Fields, Vaddeswaram, Andhra Pradesh, India, 522302
7 Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur District, Andhra Pradesh, India, 522302
* Corresponding author: Ananda.ravuri@intel.com, ananda.ravuri@gmail.com
Protection of the Internet of Things (IoT) has become a significant concern due to the widespread use of IoT technologies. Conventional Intrusion Detection Systems (IDS) have challenges when used in IoT networks because of resource restrictions and complexities. Blockchain Technology (BCT) has significantly altered organizations' financial behavior and effectiveness in recent years. Data security and system stability are crucial concerns that must be tackled in blockchain systems. The study suggests a mechanism called Deep Blockchain-Enabled Collaborative Anomaly Detection (DBC-CAD) for security-focused distributed Anomaly Detection (AD) and privacy-focused BC with smart contracts in IoT networks. A Modified - Long Short-Term Memory (M-LSTM) based Deep Learning (DL) algorithm with a multi-variable optimization approach has been used for the AD approach. The multi-variable optimization technique has been used to set the hyperparameters. The Ethereum framework creates privacy-focused BC and smart contract techniques that safeguard decentralized AD engines. The proposed M-LSTM model has the highest detection rate of 99.1%. The findings show the effectiveness of the proposed systems in identifying assaults on IoT networks.
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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