International Journal of Image, Graphics and Signal Processing (IJIGSP)
IJIGSP Vol. 14, No. 5, 8 Oct. 2022
Cover page and Table of Contents: PDF (size: 575KB)
MLSMBQS: Design of a Machine Learning Based Split & Merge Blockchain Model for QoSAware Secure IoT Deployments
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Author(s)
Shital Agrawal
1,*
Shailesh Kumar
2
Index Terms
IoT, Blockchain, Sidechain, Machine Learning, QoS, PoW, EHO, Delay, Security, Energy, Attacks.
Abstract
Internet of Things (IoT) Networks are multitier deployments which assist on-field data to be sensed, processed, communicated, and used for taking control decisions. These deployments utilize hardware-based components for data sensing & actuation, while cloud components are used for data-processing & recommending control decisions. This process involves multiple low-security, low-computational capacity & high-performance entities like IoT Devices, short range communication interfaces, edge devices, routers, & cloud virtual machines. Out of these entities, the IoT Device, router, & short-range communication interfaces are highly vulnerable to a wide-variety of attacks including Distributed Denial of Service (DDoS), worm hole, sybil, Man in the Middle (MiTM), Masquerading, spoofing attacks, etc. To counter these attacks, a wide variety of encryption, key-exchange, and data modification models are proposed by researchers. Each of these models have their own levels of complexities, which reduces QoS of underlying IoT deployments. To overcome this limitation, blockchain-based security models were proposed by researchers, and these models allow for high-speed operations for small-scale networks. But as network size is increased, delay needed for blockchain mining increases exponentially, which limits its applicability. To overcome this issue, a machine learning based blockchain model for QoS-aware secure IoT deployments is proposed in this text. The proposed MLSMBQS model initially deploys a Proof-of-Work (PoW) based blockchain model, and then uses bioinspired computing to split the chain into multiple sub-chains. These sub-chains are termed as shards, and assists in reduction of mining delay via periodic chain splitting process. The significance of this research is use of Elephant Herd Optimization (EHO) which assists in managing number of blockchain-shards via splitting or merging them for different deployment conditions. This decision of splitting or merging depends on blockchain’s security & quality of service (QoS) performance. Due to integration of EHO for creation & management of sidechains, the findings of this research showcase that the proposed model is capable of improving throughput by 8.5%, reduce communication delay by 15.3%, reduce energy consumption by 4.9%, and enhance security performance by 14.8% when compared with existing blockchain & non-blockchain based security models. This is possible because EHO initiates dummy communication requests, which are arbitrarily segregated into malicious & non-malicious, and usedfor continuous QoS & security performance improvement of the proposed model. Due to this continuous performance improvement, the proposed MLSMBQS model is capable of deployment for a wide variety of high-efficiency IoT network scenarios.
Cite This Paper
Shital Agrawal, Shailesh Kumar, " MLSMBQS: Design of a Machine Learning Based Split & Merge Blockchain Model for QoS-Aware Secure IoT Deployments", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.14, No.5, pp. 58-71, 2022. DOI:10.5815/ijigsp.2022.05.05
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