Security System for IoT Applications Based on Hybrid Fuzzy Possibility C-Means Clustering

With improved applications in a variety of industries, the Internet of Things (IoT) and distributed ledger innovation are captivating services and industrial advancements. The IoT and blockchain industries are expected to grow by a factor of three by 2025. As computational power increases, many IoT...

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Published in:2024 International Conference on Integrated Circuits, Communication, and Computing Systems (ICIC3S) Vol. 1; pp. 1 - 5
Main Authors: Parmar, Monika, Kaur, Navneet, Tripathy, Ramamani, Singh, Hakam, Gupta, Lipika
Format: Conference Proceeding
Language:English
Published: IEEE 08.06.2024
Subjects:
Blockchain Technology
Byzantine fault tolerance
Clustering algorithm
Clustering algorithms
Fault tolerance
Fault tolerant systems
Industries
IoT
Proof of Work
Routing
Scalability
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Abstract With improved applications in a variety of industries, the Internet of Things (IoT) and distributed ledger innovation are captivating services and industrial advancements. The IoT and blockchain industries are expected to grow by a factor of three by 2025. As computational power increases, many IoT applications confront substantial challenges with regard to data security and scalability. Additionally, Proof-of-work suffers from a number of problems that make it impractical for IoT applications, therefore Practical Byzantine Fault Tolerance (PBFT) is a widely utilized type of decentralized agreement. Nevertheless, the network node's trust in PBFT is not assured, and the approach to achieving consensus consumes a substantial quantity of network resources. This research proposes a new consensus mechanism known as a Hybrid consensus blockchain algorithm. This includes the features that provide safety into the clustering algorithm for identifying and classifying attackers. To promote cluster diversity in wireless ad hoc networks, a technique called Hybrid fuzzy Possibility C Means Clustering (HFPCM) is developed. For detecting and organizing the intruders in order to create the organization more attack resilient, a Byzantine agreement based cooperative methodology is used.
AbstractList With improved applications in a variety of industries, the Internet of Things (IoT) and distributed ledger innovation are captivating services and industrial advancements. The IoT and blockchain industries are expected to grow by a factor of three by 2025. As computational power increases, many IoT applications confront substantial challenges with regard to data security and scalability. Additionally, Proof-of-work suffers from a number of problems that make it impractical for IoT applications, therefore Practical Byzantine Fault Tolerance (PBFT) is a widely utilized type of decentralized agreement. Nevertheless, the network node's trust in PBFT is not assured, and the approach to achieving consensus consumes a substantial quantity of network resources. This research proposes a new consensus mechanism known as a Hybrid consensus blockchain algorithm. This includes the features that provide safety into the clustering algorithm for identifying and classifying attackers. To promote cluster diversity in wireless ad hoc networks, a technique called Hybrid fuzzy Possibility C Means Clustering (HFPCM) is developed. For detecting and organizing the intruders in order to create the organization more attack resilient, a Byzantine agreement based cooperative methodology is used.
Author Singh, Hakam
Kaur, Navneet
Parmar, Monika
Tripathy, Ramamani
Gupta, Lipika
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  surname: Parmar
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  givenname: Navneet
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  givenname: Hakam
  surname: Singh
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  givenname: Lipika
  surname: Gupta
  fullname: Gupta, Lipika
  email: lipikagupta21@gmail.com
  organization: Chitkara University Institute of Engineering and Technology, Chitkara University,Punjab,India
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SubjectTerms Blockchain Technology
Byzantine fault tolerance
Clustering algorithm
Clustering algorithms
Fault tolerance
Fault tolerant systems
Industries
IoT
Proof of Work
Routing
Scalability
Title Security System for IoT Applications Based on Hybrid Fuzzy Possibility C-Means Clustering
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