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| Title | Securechainai: Integrating Blockchain And Artificial Intelligence For Enhanced Security In Iot Environments |
|---|---|
| ID_Doc | 47699 |
| Authors | Thamaraiselvi K.; Pushpalatha A.; Chidambarathanu K.; Wankhede J.P.; Alagumuthukrishnan S.; Sarveshwaran V. |
| Year | 2024 |
| Published | Proceedings of the 5th International Conference on Smart Electronics and Communication, ICOSEC 2024 |
| DOI | http://dx.doi.org/10.1109/ICOSEC61587.2024.10722669 |
| Abstract | Internet of Things (IoT) systems develop with paramount importance for security in view of the increased deployment of IoT devices over critical sectors like healthcare, industry automation, and smart cities. The risk exposure of such systems is extremely high to potent cyber threats, including those that may result in data integrity and privacy issues and operational unreliability. Security in IoT systems has thus emerged through heterogeneous integration with blockchain and artificial intelligence models. This blockchain technology provides a decentralized, immutable ledger for secure data transactions. AI models, especially those designed by anomaly detection and prediction algorithms, can identify and mitigate cyber threats. In this paper, the authors propose the SecureChainAI model that synergistically integrates artificial intelligence algorithms with blockchain to safeguard IoT environments. The two core building blocks that will make this proposed model real are CaSNet and the EchoChain algorithm. The CaSNet model integrates LSTM, RNN, and GRU techniques with novelty detection for proficient attack prediction and detection using their peculiar capabilities in sequential data handling and the identification of novel attack patterns. The EchoChain algorithm puts blockchain technology into the IoT infrastructure to provide security in communication and integrity of the values. This makes the architecture lightweight, shifting computational loads to the collector node, and hence suitable for IoT devices with low computational resources. Evaluations of the performance necessary to detect different classes of cyberattacks using SecureChainAI have improved on the detection accuracy, precision, recall, f1-score, and reduced error rates. © 2024 IEEE. |
| Author Keywords | and Intrusion; Artificial Intelligence (AI); Blockchain; Deep Learning; Internet of Things (IoT); Security |
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