A highly accurate machine learning approach for developing wireless sensor network middleware

Alshinina, Remah; Elleithy, Khaled

doi:10.1109/wts.2018.8363955

Cited by 11 publications

(18 citation statements)

References 27 publications

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“…The main contributions of the work are following: Deep Extreme Learning Machine (RTS-DELM) framework is better as compare to other algorithms in terms of accuracy and miss rate such as support vector machine [48], selforganization map [49], artificial neural network-based intrusion detection system [50], discriminative multinomial naïve bayes [51] and Generative adversarial networks (GANs) [52].…”

Section: Related Workmentioning

confidence: 99%

Data Fusion-Based Machine Learning Architecture for Intrusion Detection

Khan¹,

Ghazal²,

Lee³

et al. 2022

Computers, Materials &Amp; Continua

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In recent years, the infrastructure of Wireless Internet of Sensor Networks (WIoSNs) has been more complicated owing to developments in the internet and devices' connectivity. To effectively prepare, control, hold and optimize wireless sensor networks, a better assessment needs to be conducted. The field of artificial intelligence has made a great deal of progress with deep learning systems and these techniques have been used for data analysis. This study investigates the methodology of Real Time Sequential Deep Extreme Learning Machine (RTS-DELM) implemented to wireless Internet of Things (IoT) enabled sensor networks for the detection of any intrusion activity. Data fusion is a well-known methodology that can be beneficial for the improvement of data accuracy, as well as for the maximizing of wireless sensor networks lifespan. We also suggested an approach that not only makes the casting of parallel data fusion network but also render their computations more effective. By using the Real Time Sequential Deep Extreme Learning Machine (RTS-DELM) methodology, an excessive degree of reliability with a minimal error rate of any intrusion activity in wireless sensor networks is accomplished. Simulation results show that wireless sensor networks are optimized effectively to monitor and detect any malicious or intrusion activity through this proposed approach. Eventually, threats and a more general outlook are explored.

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Section: Related Workmentioning

confidence: 99%

Data Fusion-Based Machine Learning Architecture for Intrusion Detection

Khan¹,

Ghazal²,

Lee³

et al. 2022

Computers, Materials &Amp; Continua

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“…a) The machine learning (ML) application and its supervised and unsupervised techniques in the design of protocols and security mechanisms for WSNs are reflected in the proposals that allow optimizing lifespans of ultra-dense WSNs, which balance energy consumption [57]. Similarly, [58] studies developed WSN middleware to provide a secure end-to-end system, which significantly decreased power consumption. b) Improved authentication schemes are analyzed [59] through an improved scheme based on symmetric cryptography for IoT systems that integrated WSNs.…”

Section: Trends and Future Lines Of Workmentioning

confidence: 99%

Prospectiva de seguridad de las redes de sensores inalámbricos

et al. 2021

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In Wireless Sensor Networks (WSN), nodes are vulnerable to security attacks because they are installed in a harsh environment with limited power and memory, low processing power, and medium broadcast transmission.Therefore, identifying threats, challenges, and solutions of security and privacy is a talkingtopic today. This article analyzes the research work that has been carried out on the security mechanisms for the protection of WSN against threats and attacks, as well as the trends that emerge in other countries combined with future research lines. From the methodological point of view, this analysis is shown through the visualization and study of works indexed in databases such as IEEE, ACM, Scopus,and Springer, with a range of 7 years as an observation window, from 2013 to 2019. A total of 4,728 publications were obtained, with a high rate of collaborationbetween China and India. The research raised developments, such as advances in security principles and defense mechanisms, which have led to the design of countermeasures in intrusion detection. Finally, the results show the interest of the scientific andbusiness community in the use of artificial intelligence and machine learning (ML) to optimize performance measurements.

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“…83 Leveraging SOA for WSNs There has been a lot of developments in service-oriented computing paradigm using services as the central element of the design. One of the major goals of any technology is to provide services to the users, and such approach was proposed by Kim et al 84 and Alshinina and Elleithy, 85 known as SOA; SOA has evolved in the last decades with specific details with many successful implementations. SOA is defined as an architectural framework having three fundamental roles, namely, service providers, service consumers, and service broker ( Figure 23).…”

Section: Middleware Approaches For Wsnsmentioning

confidence: 99%

Evolution of wireless sensor network design from technology centric to user centric: An architectural perspective

Singh

Luhach

Gao

et al. 2020

International Journal of Distributed Sensor Networks

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Wireless sensor networks form the crux of a wide range of automated applications that are gaining popularity with every passing day. The journey of wireless sensor networks has seen simple sensory monitoring to sensing-processing-actuation based end-to-end applications to the design and deployment of highly sophisticated service-oriented use cases. This highly disruptive field has seen rapid transformations to gain insights and assess the relative merits. Demerits of competing design strategies, it very important to know the evolutionary milestones this technology has undergone in achieving the state-of-the-art in this area. In this article, a systematic review is carried out that captures the evolution of architectural designs and developments in wireless sensor network–based applications. This review delves into the relative pros and cons of various epochs in developments and delineates the future areas of research in wireless sensor network design paradigm.

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A highly accurate machine learning approach for developing wireless sensor network middleware

Cited by 11 publications

References 27 publications

Data Fusion-Based Machine Learning Architecture for Intrusion Detection

Data Fusion-Based Machine Learning Architecture for Intrusion Detection

Prospectiva de seguridad de las redes de sensores inalámbricos

Evolution of wireless sensor network design from technology centric to user centric: An architectural perspective

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