FATMLPGS: Design of a fault-aware trust establishment model for low-power IoT deployments via generic lightweight sidechains

DoS, GH, Sybil, Masquerading, Spoofing, Man in the Middle, etc. constantly attack IoT networks. Internal or external attacks reduce end-to-end delay, throughput, energy use, and other metrics. To counter these attacks, researchers have proposed a number of security & privacy mechanisms with vary...

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Bibliographic Details
Published in:Journal of intelligent & fuzzy systems 2023-06, Vol.44 (6), p.9183-9201
Main Authors: Choudhary, Ashutosh Kumar, Rahamatkar, Surendra
Format: Article
Language:English
Subjects:
Blockchain
Complexity
Cryptography
Energy consumption
Fault detection
Genetic algorithms
Lightweight
Machine learning
Power consumption
Quality of service architectures
Security
Spoofing
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Summary:DoS, GH, Sybil, Masquerading, Spoofing, Man in the Middle, etc. constantly attack IoT networks. Internal or external attacks reduce end-to-end delay, throughput, energy use, and other metrics. To counter these attacks, researchers have proposed a number of security & privacy mechanisms with varying computational complexity and security levels. Immutability, traceability, transparency, and distributed nature make blockchain-based models secure. QoS depends on blockchain length, so these models aren’t scalable. Researchers say sidechaining improves QoS while remaining secure. Splitting or merging complex sidechains requires machine learning. Low-power IoT networks can’t use models. This text suggests a lightweight MGWO Model that helps establish initial routes by choosing high-trust nodes, reducing sidechaining power consumption, and incorporating fault-aware trust establishment. MGWO Model determines blockchain piece count for high QoS. MGWO Model uses Q-Learning to detect network faults. Fault identification is controlled by a stochastically modelled and activated Intrinsic Genetic Algorithm (IGA). Q-Learning, MGWO, and IGA can mitigate Sybil, Masquerading, Grey Hole, DDoS, and MITM attacks. Even when attacked, the proposed model maintains high QoS, improving real-time deployment efficiency. The proposed model improves energy efficiency by 15.9%, throughput by 10.6%, communication speed by 8.3%, and packet delivery by 0.8% for different network scenarios.
ISSN:1064-1246
1875-8967
DOI:10.3233/JIFS-223316