Abstract
Internet of Things (IoT) is playing a vital role in the smart infrastructure environment. The IoT vendors are delivering their products in the market without any concern about the security of the devices, so it is an open number of security issues on the IoT devices and data. Security threats are growing high because existing techniques measures are inadequate; two of the most significant concerns in IoT are security and privacy. Due to the IoT devices limited CPU, storage, and energy resources, existing security architectures are unable to provide the key safety requirements, so deep learning and blockchain algorithms are used. These IoT devices give accurate results from heavy complex datasets. Furthermore, blockchain and deep learning model are very familiar to give secured devices to IoT. This proposed model is window blockchain (WBC). In proof-of-work, it leverages past (n − 1) hash to construct the next hash with minimal change; because of this quick block analysis time, we can easily prevent IoT devices from the attackers. WBC’s performance is evaluated using an actual data stream generated by one of the analyzed smart infrastructure devices. Another method using deep learning hybrid algorithms for LSTM networks with whale optimization algorithm is a new schema optimization technique that mimics humpback whales’ intelligent bubble-net fishing activity. It is an easy, powerful, and predator probabilistic optimization algorithm that can avoid local optima and find global optimal answer. The findings indicate that the proposed window blockchain model improves security and reduces memory utilization this employing limited resources. In the Whale +LSTM (WLSTM), a large number of the dataset were gathered using a real-time scenario using OMNET++IoT plugins, and a Python API is created to insert various malicious activity through networks. The proposed WLSTM model output of 99% has been tested and related to other deep learning utilizing benchmark datasets such as CIDDC-001, UNSWN15, and KDD datasets, as well as actual datasets; the prediction of unknown threats is used to tackle the computation complexity in big networks.
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Janani, K., Ramamoorthy, S. (2022). Integrated Smart IoT Infrastructure Management Using Window Blockchain and Whale LSTM Approaches. In: Goar, V., Kuri, M., Kumar, R., Senjyu, T. (eds) Advances in Information Communication Technology and Computing. Lecture Notes in Networks and Systems, vol 392. Springer, Singapore. https://doi.org/10.1007/978-981-19-0619-0_10
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