Smart Contract-Enhanced Residual GRU with Merkle-Damgard Cryptography for IoT Attack Detection
Received: 30 August 2024 | Revised: 18 September 2024| Accepted: 15 November 2024 | Online: 2 February 2025
Corresponding author: T. Nishitha
Abstract
As IoT continues to expand, the security of connected devices remains a critical concern, particularly in the face of DDoS attacks. This study introduces a novel approach that leverages blockchain technology through smart contracts integrated with an advanced attack detection mechanism. Central to this approach is the Enhanced Residual Gated Recurrent Unit (ERGRU) architecture, designed to effectively identify and mitigate DDoS attacks within IoT networks. The Adaptive Coati Optimization Algorithm (ACOA) was used to adjust the hyperparameters of the ERGRU model, such as the learning rate and the number of GRU neurons, to further improve detection accuracy. In addition, the proposed framework uses a one-way compression function to generate secure hashes for input data, utilizing the Merkle-Damgård cryptography technique to ensure data integrity and confidentiality. The proposed solution was tested through a rigorous process using a DDoS dataset. Performance was assessed by focusing on metrics such as processing time, data integrity rate, and confidentially rate. The results demonstrate the effectiveness of the proposed smart contract-based framework in providing a durable and efficient protection mechanism against DDoS attacks in IoT environments.
Keywords:
IoT, distributed denial of service, residual gated recurrent unit, Coati optimization algorithm, Merkle-Damgård cryptographic techniqueDownloads
References
J. Tournier, F. Lesueur, F. L. Mouël, L. Guyon, and H. Ben-Hassine, "A survey of IoT protocols and their security issues through the lens of a generic IoT stack," Internet of Things, vol. 16, Dec. 2021, Art. no. 100264.
"Number of connected IoT devices growing 13% to 18.8 billion." https://iot-analytics.com/number-connected-iot-devices/.
I. Ali et al., "Systematic Literature Review on IoT-Based Botnet Attack," IEEE Access, vol. 8, pp. 212220–212232, 2020.
S. Ghazanfar, F. Hussain, A. U. Rehman, U. U. Fayyaz, F. Shahzad, and G. A. Shah, "IoT-Flock: An Open-source Framework for IoT Traffic Generation," in 2020 International Conference on Emerging Trends in Smart Technologies (ICETST), Karachi, Pakistan, Mar. 2020, pp. 1–6.
B. Ghimire and D. B. Rawat, "Recent Advances on Federated Learning for Cybersecurity and Cybersecurity for Federated Learning for Internet of Things," IEEE Internet of Things Journal, vol. 9, no. 11, pp. 8229–8249, Jun. 2022.
K. S. Kumar, S. Alqarni, S. Islam, and M. A. Shah, "Royal Poinciana Biodiesel Blends with 1-Butanol as a Potential Alternative Fuel for Unmodified Research Engines," ACS Omega, vol. 9, no. 12, pp. 13960–13974, Mar. 2024.
Kamaldeep, M. Malik, and M. Dutta, "Feature Engineering and Machine Learning Framework for DDoS Attack Detection in the Standardized Internet of Things," IEEE Internet of Things Journal, vol. 10, no. 10, pp. 8658–8669, Feb. 2023.
F. Hussain, S. G. Abbas, M. Husnain, U. U. Fayyaz, F. Shahzad, and G. A. Shah, "IoT DoS and DDoS Attack Detection using ResNet," in 2020 IEEE 23rd International Multitopic Conference (INMIC), Bahawalpur, Pakistan, Nov. 2020, pp. 1–6.
Y. N. Soe, Y. Feng, P. I. Santosa, R. Hartanto, and K. Sakurai, "Machine Learning-Based IoT-Botnet Attack Detection with Sequential Architecture," Sensors, vol. 20, no. 16, Jan. 2020, Art. no. 4372.
N. Vlajic and D. Zhou, "IoT as a Land of Opportunity for DDoS Hackers," Computer, vol. 51, no. 7, pp. 26–34, Jul. 2018.
M. Aslam, D. Ye, M. Hanif, and M. Asad, "Machine Learning Based SDN-enabled Distributed Denial-of-Services Attacks Detection and Mitigation System for Internet of Things," in Machine Learning for Cyber Security, Cham, 2020, pp. 180–194.
J. Gubbi, R. Buyya, S. Marusic, and M. Palaniswami, "Internet of Things (IoT): A vision, architectural elements, and future directions," Future Generation Computer Systems, vol. 29, no. 7, pp. 1645–1660, Sep. 2013.
R. Taylor, D. Baron, and D. Schmidt, "The world in 2025 - predictions for the next ten years," in 2015 10th International Microsystems, Packaging, Assembly and Circuits Technology Conference (IMPACT), Taipei, Taiwan, Oct. 2015, pp. 192–195.
S. K. Dash et al., "Enhancing DDoS attack detection in IoT using PCA," Egyptian Informatics Journal, vol. 25, Mar. 2024, Art. no. 100450.
H. Al-Hamadi, I.-R. Chen, D.-C. Wang, and M. Almashan, "Attack and Defense Strategies for Intrusion Detection in Autonomous Distributed IoT Systems," IEEE Access, vol. 8, pp. 168994–169009, 2020.
F. Hussain et al., "A Two-Fold Machine Learning Approach to Prevent and Detect IoT Botnet Attacks," IEEE Access, vol. 9, pp. 163412–163430, 2021.
J. Bhayo, S. Hameed, and S. A. Shah, "An Efficient Counter-Based DDoS Attack Detection Framework Leveraging Software Defined IoT (SD-IoT)," IEEE Access, vol. 8, pp. 221612–221631, 2020.
Y. Alhasawi and S. Alghamdi, "Federated Learning for Decentralized DDoS Attack Detection in IoT Networks," IEEE Access, vol. 12, pp. 42357–42368, 2024.
"DDoS Dataset." Kaggle, [Online]. Available: https://www.kaggle.com/datasets/devendra416/ddos-datasets.
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