A Machine Learning Approach for Blockchain-Based Smart Home Networks Security

Khan, Muhammad Adnan; Abbas, Sagheer; Rehman, Abdur; Saeed, Yousaf; Zeb, Asim; Uddin, Imad; Nasser, Nidal; Ali, Asmaa

doi:10.1109/mnet.011.2000514

Cited by 83 publications

(43 citation statements)

References 11 publications

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“…Machine learning arose over the last two decades from the increasing capacity of computers to process large amounts of data. Computational Intelligence approaches like Swarm Intelligence [16], Evolutionary Computing [17] like Genetic Algorithm [18], Neural Network [19], Deep Extreme Machine learning [20] and Fuzzy system [21][22][23][24][25][26][27] are strong candidate solution in the field of smart city [28][29][30], smart health [31][32][33], and wireless communication [34,35], etc. Some machines could also uncover secret patterns and complicated interactions that humans could not, allowing them to make appropriate and accurate judgments in the face of extraordinarily disruptive and discontinuous data.…”

Section: Adoption Of Machine Learning In Supply Chain Collaborationmentioning

confidence: 99%

Fusion-Based Supply Chain Collaboration Using Machine Learning Techniques

Ali¹,

Ghazal²,

Ahmed³

et al. 2022

Intelligent Automation &Amp; Soft Computing

Self Cite

169

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Supply Chain Collaboration is the network of various entities that work cohesively to make up the entire process. The supply chain organizations' success is dependent on integration, teamwork, and the communication of information. Every day, supply chain and business players work in a dynamic setting. They must balance competing goals such as process robustness, risk reduction, vulnerability reduction, real financial risks, and resilience against just-in-time and cost-efficiency. Decision-making based on shared information in Supply Chain Collaboration constitutes the recital and competitiveness of the collective process. Supply Chain Collaboration has prompted companies to implement the perfect data analytics functions (e.g., data science, predictive analytics, and big data) to improve supply chain operations and, eventually, efficiency. Simulation and modeling are powerful methods for analyzing, investigating, examining, observing and evaluating real-world industrial and logistic processes in this scenario. Fusion-based Machine learning provides a platform that may address the issues/limitations of Supply Chain Collaboration. Compared to the Classical probable data fusion techniques, the fused Machine learning method may offer a strong computing ability and prediction. In this scenario, the machine learningbased Supply Chain Collaboration model has been proposed to evaluate the propensity of the decision-making process to increase the efficiency of the Supply Chain Collaboration.

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Section: Adoption Of Machine Learning In Supply Chain Collaborationmentioning

confidence: 99%

Fusion-Based Supply Chain Collaboration Using Machine Learning Techniques

Ali¹,

Ghazal²,

Ahmed³

et al. 2022

Intelligent Automation &Amp; Soft Computing

Self Cite

169

View full text Add to dashboard Cite

show abstract

“…Deep & Machine learning arose over the last two decades from the increasing capacity of computers to process large amounts of data empowered with cloud computing [27,28]. Computational Intelligence approaches like Swarm Intelligence [29], Evolutionary Computing [30] like Genetic Algorithm [31], Neural Network [32], Deep Extreme Machine learning [33] and Fuzzy system [34][35][36][37][38] are strong candidate solutions in the field of the smart city [39][40][41], smart health empowered with cloud computing [42,43], and wireless communication [44,45,46], etc.…”

Section: Software As a Service (Saas's) Qosmentioning

confidence: 99%

Intelligent Model for Predicting the Quality of Services Violation

Khan¹,

Kanwal²,

Abbas³

et al. 2022

Computers, Materials &Amp; Continua

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Cloud computing is providing IT services to its customer based on Service level agreements (SLAs). It is important for cloud service providers to provide reliable Quality of service (QoS) and to maintain SLAs accountability. Cloud service providers need to predict possible service violations before the emergence of an issue to perform remedial actions for it. Cloud users' major concerns; the factors for service reliability are based on response time, accessibility, availability, and speed. In this paper, we, therefore, experiment with the parallel mutant-Particle swarm optimization (PSO) for the detection and predictions of QoS violations in terms of response time, speed, accessibility, and availability. This paper also compares Simple-PSO and Parallel Mutant-PSO. In simulation results, it is observed that the proposed Parallel Mutant-PSO solution for cloud QoS violation prediction achieves 94% accuracy which is many accurate results and is computationally the fastest technique in comparison of conventional PSO technique.

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“…By utilizing unique computing resources in a regular IoT space and applying an instance of extreme learning machine (ELM), a blockchain-based efficient solution for safe and secure IoT was proposed by Khan et al [75]. This approach analyzes the credibility of the blockchain-based smart home in terms of the fundamental security objectives of confidentiality, accessibility, and integrity.…”

Section: Detecting Attacks Over Iot Networkmentioning

confidence: 99%

Intelligent Techniques for Detecting Network Attacks: Review and Research Directions

Aljabri

Aljameel

Mohammad

et al. 2021

Sensors

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The significant growth in the use of the Internet and the rapid development of network technologies are associated with an increased risk of network attacks. Network attacks refer to all types of unauthorized access to a network including any attempts to damage and disrupt the network, often leading to serious consequences. Network attack detection is an active area of research in the community of cybersecurity. In the literature, there are various descriptions of network attack detection systems involving various intelligent-based techniques including machine learning (ML) and deep learning (DL) models. However, although such techniques have proved useful within specific domains, no technique has proved useful in mitigating all kinds of network attacks. This is because some intelligent-based approaches lack essential capabilities that render them reliable systems that are able to confront different types of network attacks. This was the main motivation behind this research, which evaluates contemporary intelligent-based research directions to address the gap that still exists in the field. The main components of any intelligent-based system are the training datasets, the algorithms, and the evaluation metrics; these were the main benchmark criteria used to assess the intelligent-based systems included in this research article. This research provides a rich source of references for scholars seeking to determine their scope of research in this field. Furthermore, although the paper does present a set of suggestions about future inductive directions, it leaves the reader free to derive additional insights about how to develop intelligent-based systems to counter current and future network attacks.

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A Machine Learning Approach for Blockchain-Based Smart Home Networks Security

Cited by 83 publications

References 11 publications

Fusion-Based Supply Chain Collaboration Using Machine Learning Techniques

Fusion-Based Supply Chain Collaboration Using Machine Learning Techniques

Intelligent Model for Predicting the Quality of Services Violation

Intelligent Techniques for Detecting Network Attacks: Review and Research Directions

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