Blockchain-Based Smart Home Networks Security Empowered with Fused Machine Learning

Farooq, Muhammad; Khan, Safiullah; Rehman, Abdur; Abbas, Sagheer; Khan, Muhammad Adnan; Hwang, Seong Oun

doi:10.3390/s22124522

Cited by 25 publications

(15 citation statements)

References 17 publications

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“…This research presented advances in ANNs, image processing, multipurpose decision-making, and blurred linguistic variables. Furthermore, automated systems have been confirmed to improve the performance of supply chain administration systems and increase corporate performance [24][25][26]. It was also demonstrated that the growing function of automated arrangement necessitates capital reserves for R & D activities.…”

Section: Literature Reviewmentioning

confidence: 98%

Network Intrusion Detection Model Using Fused Machine Learning Technique

Alotaibi¹

2023

Computers, Materials &Amp; Continua

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With the progress of advanced technology in the industrial revolution encompassing the Internet of Things (IoT) and cloud computing, cyberattacks have been increasing rapidly on a large scale. The rapid expansion of IoT and networks in many forms generates massive volumes of data, which are vulnerable to security risks. As a result, cyberattacks have become a prevalent and danger to society, including its infrastructures, economy, and citizens' privacy, and pose a national security risk worldwide. Therefore, cyber security has become an increasingly important issue across all levels and sectors. Continuous progress is being made in developing more sophisticated and efficient intrusion detection and defensive methods. As the scale of complexity of the cyber-universe is increasing, advanced machine learning methods are the most appropriate solutions for predicting cyber threats. In this study, a fused machine learning-based intelligent model is proposed to detect intrusion in the early stage and thus secure networks from harmful attacks. Simulation results confirm the effectiveness of the proposed intrusion detection model, with 0.909 accuracy and a miss rate of 0.091.

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Section: Literature Reviewmentioning

confidence: 98%

Network Intrusion Detection Model Using Fused Machine Learning Technique

Alotaibi¹

2023

Computers, Materials &Amp; Continua

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“…This can be crucial for analyzing network participants to represent vulnerabilities, strengthen network security, evolve countermeasures, and provide active security strategies. Previous studies on 51% of attacks can provide the fundamentals to improve security, governance framework, developing mechanisms, monitoring, and detecting systems 84 . By gaining knowledge through these case studies, we can identify vulnerabilities, patterns, and best practices to develop more resilient blockchain networks.…”

Section: Impact On Decentralization and Consensusmentioning

confidence: 99%

“…These algorithms can provide a comprehensive and proactive approach that enhances the capabilities of blockchain security. An unsupervised learning algorithm requires expert parametric tuning to perform precise and significant outcomes 84 . In cluster algorithm selection, several clusters or thresholds for anomaly detection can significantly influence the algorithm's efficiency.…”

Section: Unsupervised Learning Algorithmsmentioning

confidence: 99%

Blockchain security enhancement: an approach towards hybrid consensus algorithms and machine learning techniques

Venkatesan,

Rahayu

2024

Sci Rep

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In this paper, we propose hybrid consensus algorithms that combine machine learning (ML) techniques to address the challenges and vulnerabilities in blockchain networks. Consensus Protocols make ensuring agreement among the applicants in the distributed systems difficult. However, existing mechanisms are more vulnerable to cyber-attacks. Previous studies extensively explore the influence of cyber attacks and highlight the necessity for effective preventive measures. This research presents the integration of ML techniques with the proposed hybrid consensus algorithms and advantages over predicting cyber-attacks, anomaly detection, and feature extraction. Our hybrid approaches leverage and optimize the proposed consensus protocols' security, trust, and robustness. However, this research also explores the various ML techniques with hybrid consensus algorithms, such as Delegated Proof of Stake Work (DPoSW), Proof of Stake and Work (PoSW), Proof of CASBFT (PoCASBFT), Delegated Byzantine Proof of Stake (DBPoS) for security enhancement and intelligent decision making in consensus protocols. Here, we also demonstrate the effectiveness of the proposed methodology within the decentralized networks using the ProximaX blockchain platform. This study shows that the proposed research framework is an energy-efficient mechanism that maintains security and adapts to dynamic conditions. It also integrates privacy-enhancing features, robust consensus mechanisms, and ML approaches to detect and prevent security threats. Furthermore, the practical implementation of these ML-based hybrid consensus models faces significant challenges, such as scalability, latency, throughput, resource requirements, and potential adversarial attacks. These challenges must be addressed to ensure the successful implementation of the blockchain network for real-world scenarios.

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“…This importance is accentuated by the extensive reach of IoT frameworks. Farooq et al [32] have developed an innovative architecture for a private blockchain-based smart home network, utilizing a cutting-edge Fused Real-Time Sequential Deep Extreme Learning Machine (RTS-DELM) system. This method significantly reinforces smart home security by facilitating the precise identification and thwarting of unauthorized actions.…”

Section: A Real-world Examples and Use Casesmentioning

confidence: 99%

A Survey of Blockchain Integration with IoT: Benefits, Challenges and Future Directions

MAADALLAH,

KASSIMI,

EL BOUZEKRI EL IDRISSI

et al. 2024

IJCDS

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The Internet of Things (IoT) stands at the forefront of the latest generation of information technology advancements, signifying a crucial juncture in the integration of digital and physical domains. This integration is pivotal, facilitating a plethora of transformative digital services that substantially enhance user experiences by seamlessly melding virtual and physical elements. However, the rapid proliferation of IoT also brings to the fore a range of challenges, particularly in the realms of security, simplicity, and data integration. These challenges pose significant obstacles to the full realization of IoT's potential. This paper provides a comprehensive analysis of these challenges within the IoT framework. We delve into the intricacies of IoT environments, examining the security risks and integration complexities that arise. The paper proposes effective strategies to address these challenges, aiming to establish a more secure and reliable IoT infrastructure. We discuss innovative approaches for enhancing data integration and simplifying IoT systems while ensuring robust security measures are in place. Through this exploration, the paper aims to contribute to the ongoing development of IoT, offering insights into overcoming its current limitations and outlining a path for its future evolution. Our analysis underscores the need for continuous research and innovation in this field, setting the stage for the emergence of more advanced, secure, and user-friendly IoT applications across various industries.

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Blockchain-Based Smart Home Networks Security Empowered with Fused Machine Learning

Cited by 25 publications

References 17 publications

Network Intrusion Detection Model Using Fused Machine Learning Technique

Network Intrusion Detection Model Using Fused Machine Learning Technique

Blockchain security enhancement: an approach towards hybrid consensus algorithms and machine learning techniques

A Survey of Blockchain Integration with IoT: Benefits, Challenges and Future Directions

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