Stochastic Gradient Descent Intrusions Detection for Wireless Sensor Network Attack Detection System Using Machine Learning

被引:32
作者
Saleh, Hadeel M. [1 ]
Marouane, Hend [2 ]
Fakhfakh, Ahmed [3 ]
机构
[1] Univ Anbar, Continuing Educ Ctr, Ramadi 31006, Iraq
[2] Safax Univ, Natl Sch Elect & Telecommun, NTSCOM Lab, ENETCOM, Sfax 3029, Tunisia
[3] Digital & Numer Res Ctr Safax CRNS, Sfax 3029, Tunisia
关键词
Intrusion detection; wireless sensor network; machine learning; accuracy; Internet of Things;
D O I
10.1109/ACCESS.2023.3349248
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Communication in cyber-physical systems relies heavily on Wireless Sensor Networks (WSNs), which have numerous uses including ambient monitoring, object recognition, and data transmission. However, they are vulnerable to cyberattacks because they are connected to the IoT. In order to combat the difficulties associated with WSN intrusion detection, this research employs machine learning techniques, notably the Gaussian Nave Bayes (GNB) and Stochastic Gradient Descent (SGD) algorithms. The effectiveness of recommendation systems is improved with the introduction of context awareness. To lessen the burden on the computer, we first do a principal component analysis and singular value decomposition on the raw traffic data. On the WSN-DS dataset, the suggested SG-IDS model achieved a 96% accuracy rate, outperforming state-of-the-art algorithms with higher rates of 98% accuracy, 96% recall, and 97% F1 measurement. In an evaluation of an IoMT dataset, the SG-IDS performed admirably, with an accuracy of 0.87 and a precision of 1.00 in intrusion detection tasks.
引用
收藏
页码:3825 / 3836
页数:12
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