Lightweight Secure and Scalable Scheme for Data Transmission in the Internet of Things

Al-Hejri, Ibraheem; Azzedin, Farag; Almuhammadi, Sultan; Eltoweissy, Mohamed

doi:10.1007/s13369-024-08884-z

Cited by 1 publication

(2 citation statements)

References 73 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…First presented by Kevin Ashton in 1999, the Internet of Things is an extension of the internet to objects or things that are traditionally not interconnected [1,2]. With the technological advances in the decades since, the number of these devices in use has grown exponentially [3]. By 2025, it is projected that there will be around 30.9 billion connected devices which is an increase from the roughly 13.8 billion devices in 2021 [4].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Enhancing IoT Security: Optimizing Anomaly Detection through Machine Learning

Balega,

Farag,

et al. 2024

Electronics

View full text Add to dashboard Cite

As the Internet of Things (IoT) continues to evolve, securing IoT networks and devices remains a continuing challenge. Anomaly detection is a crucial procedure in protecting the IoT. A promising way to perform anomaly detection in the IoT is through the use of machine learning (ML) algorithms. There is a lack of studies in the literature identifying optimal (with regard to both effectiveness and efficiency) anomaly detection models for the IoT. To fill the gap, this work thoroughly investigated the effectiveness and efficiency of IoT anomaly detection enabled by several representative machine learning models, namely Extreme Gradient Boosting (XGBoost), Support Vector Machines (SVMs), and Deep Convolutional Neural Networks (DCNNs). Identifying optimal anomaly detection models for IoT anomaly detection is challenging due to diverse IoT applications and dynamic IoT networking environments. It is of vital importance to evaluate ML-powered anomaly detection models using multiple datasets collected from different environments. We utilized three reputable datasets to benchmark the aforementioned machine learning methods, namely, IoT-23, NSL-KDD, and TON_IoT. Our results show that XGBoost outperformed both the SVM and DCNN, achieving accuracies of up to 99.98%. Moreover, XGBoost proved to be the most computationally efficient method; the model performed 717.75 times faster than the SVM and significantly faster than the DCNN in terms of training times. The research results have been further confirmed by using our real-world IoT data collected from an IoT testbed consisting of physical devices that we recently built.

show abstract

Section: Introductionmentioning

confidence: 99%

“…As the Internet of Things continues to evolve, it poses risks and the need for greater security measures. Securing IoT networks and devices is a constant challenge as the development of the IoT is moving faster than the creation of defenses for the devices and users themselves [3,5].…”

Section: Introductionmentioning

confidence: 99%

Enhancing IoT Security: Optimizing Anomaly Detection through Machine Learning

Balega,

Farag,

et al. 2024

Electronics

View full text Add to dashboard Cite

show abstract

Lightweight Secure and Scalable Scheme for Data Transmission in the Internet of Things

Cited by 1 publication

References 73 publications

Enhancing IoT Security: Optimizing Anomaly Detection through Machine Learning

Enhancing IoT Security: Optimizing Anomaly Detection through Machine Learning

Contact Info

Product

Resources

About