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LT-FS-ID: Log-Transformed Feature Learning and Feature-Scaling-Based Machine Learning Algorithms to Predict the k-Barriers for Intrusion Detection Using Wireless Sensor Network

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Fluvial Geomorphology and Remote Sensing Laboratory, Indian Institute of Science Education and Research Bhopal, Bhopal 462066, India
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Department of Electronics and Communication Engineering, School of ICT, Gautam Buddha University, Greater Noida 201312, India
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Subir Chowdhury School of Quality and Reliability, Indian Institute of Technology, Kharagpur 721302, India
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Department of Electronics Engineering, Madhav Institute of Technology and Science, Gwalior 474005, India
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Department of Library and Information Science, Research and Development Center for Physical Education, Health, and Information Technology, Fu Jen Catholic University, New Taipei 242, Taiwan
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Department of Computer Science and Information Engineering, Asia University, Taichung 41354, Taiwan
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Authors to whom correspondence should be addressed.
Academic Editor: Antonio Guerrieri
Sensors 2022, 22(3), 1070; https://doi.org/10.3390/s22031070
Received: 11 December 2021 / Revised: 22 January 2022 / Accepted: 27 January 2022 / Published: 29 January 2022
(This article belongs to the Section Sensor Networks)
The dramatic increase in the computational facilities integrated with the explainable machine learning algorithms allows us to do fast intrusion detection and prevention at border areas using Wireless Sensor Networks (WSNs). This study proposed a novel approach to accurately predict the number of barriers required for fast intrusion detection and prevention. To do so, we extracted four features through Monte Carlo simulation: area of the Region of Interest (RoI), sensing range of the sensors, transmission range of the sensor, and the number of sensors. We evaluated feature importance and feature sensitivity to measure the relevancy and riskiness of the selected features. We applied log transformation and feature scaling on the feature set and trained the tuned Support Vector Regression (SVR) model (i.e., LT-FS-SVR model). We found that the model accurately predicts the number of barriers with a correlation coefficient (R) = 0.98, Root Mean Square Error (RMSE) = 6.47, and bias = 12.35. For a fair evaluation, we compared the performance of the proposed approach with the benchmark algorithms, namely, Gaussian Process Regression (GPR), Generalised Regression Neural Network (GRNN), Artificial Neural Network (ANN), and Random Forest (RF). We found that the proposed model outperforms all the benchmark algorithms. View Full-Text
Keywords: WSNs; intrusion detection; machine learning; feature learning; support vector regression WSNs; intrusion detection; machine learning; feature learning; support vector regression
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MDPI and ACS Style

Singh, A.; Amutha, J.; Nagar, J.; Sharma, S.; Lee, C.-C. LT-FS-ID: Log-Transformed Feature Learning and Feature-Scaling-Based Machine Learning Algorithms to Predict the k-Barriers for Intrusion Detection Using Wireless Sensor Network. Sensors 2022, 22, 1070. https://doi.org/10.3390/s22031070

AMA Style

Singh A, Amutha J, Nagar J, Sharma S, Lee C-C. LT-FS-ID: Log-Transformed Feature Learning and Feature-Scaling-Based Machine Learning Algorithms to Predict the k-Barriers for Intrusion Detection Using Wireless Sensor Network. Sensors. 2022; 22(3):1070. https://doi.org/10.3390/s22031070

Chicago/Turabian Style

Singh, Abhilash, J. Amutha, Jaiprakash Nagar, Sandeep Sharma, and Cheng-Chi Lee. 2022. "LT-FS-ID: Log-Transformed Feature Learning and Feature-Scaling-Based Machine Learning Algorithms to Predict the k-Barriers for Intrusion Detection Using Wireless Sensor Network" Sensors 22, no. 3: 1070. https://doi.org/10.3390/s22031070

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