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

Singh, Abhilash; Amutha, J.; Nagar, Jaiprakash; Sharma, Sandeep; Lee, Cheng-Chi

doi:10.3390/s22031070

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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

^4,*

and

Cheng-Chi Lee

^5,6,*

Fluvial Geomorphology and Remote Sensing Laboratory, Indian Institute of Science Education and Research Bhopal, Bhopal 462066, India

Department of Electronics and Communication Engineering, School of ICT, Gautam Buddha University, Greater Noida 201312, India

Subir Chowdhury School of Quality and Reliability, Indian Institute of Technology, Kharagpur 721302, India

⁴

Department of Electronics Engineering, Madhav Institute of Technology and Science, Gwalior 474005, India

⁵

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

⁶

Department of Computer Science and Information Engineering, Asia University, Taichung 41354, Taiwan

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)

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Abstract

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

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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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Author Biographies

Abhilash Singh

ABHILASH SINGH received an integrated dual (B.Tech. and M.Tech.) degree in electronics and communication engineering with a specialization in wireless communication and networks from Gautam Buddha University, Greater Noida, India, in 2017. He is currently pursuing a PhD degree in remote sensing with the Indian Institute of Science Education and Research at Bhopal, Bhopal, India. Since 2018, he has been working on the NASA-ISRO Synthetic Aperture Radar (NISAR) Project at IISER Bhopal. He has been publishing research papers in peer-reviewed conferences and internationally reputed journals. His current research interests include microwave remote sensing, machine learning, bio-inspired algorithms, wireless sensor networks, and wireless communication. Mr. Singh is a member of the European Geophysical Union (EGU), ISPRS, IEEE, and the Indian Radio Science Society (InRaSS). He was a recipient of the Gold Medal Awards from the University for being First Rank Holder in his UG and PG. He received the prestigious ‘‘DST-INSPIRE’’ Fellowship to carry out his PhD degree from the Department of Science and Technology (DST), Ministry of Science and Technology, India. He also received the DAAD Fellowship to attend a Summer School in 2019.

J. Amutha

Orcid id: 0000-0001-8986-5286

Jaiprakash Nagar

Born in Nagla Vasdev Village, Mathura, Uttar Pradesh, India in 1991. He received his integrated B. Tech and M. Tech degree in electronics and communication engineering and wireless communication and networks, respectively from Gautam Buddha University, Greater Noida, India, in 2015. Currently, he is pursuing his Ph.D. degree at Subir Chowdhury School of Quality and Reliability, Indian Institute of Technology, Kharagpur, India. His current research interest includes analytical modelling of wireless multihop networks, internet of things (IoTs), machine learning techniques for IoTs, and block-chain implementation for real-life applications. Mr. Nagar has published six research articles in reputed SCI/Scopus indexed journals and international conferences (IEEE/Springer/Taylor and Francis).

Sandeep Sharma

Received a B.Tech. degree in electronics engineering from RGPV, Bhopal, India, in 1997 and an M.Tech. degree in digital communication from Devi Ahilya University, Indore, India, in 2005. He received a Ph.D. in electronics and communication engineering from Gautam Buddha University, Greater Noida, India, in 2016. Currently, he is an Assistant Professor in the Department of Electronics Engineering at Madhav Institute of Technology and Science, Gwalior (M.P.), India. His research interest includes wireless sensor networks, wireless network security, physical layer authentication, intrusion detection in wireless networks, cross-layer design, and machine learning applications in WSNs.He has published 22 research papers in reputed international journals and more than 41 papers published in international conferences. Dr. Sharma is a recipient of the Best Conference Paper in the international conference ICCCS, 2016, and the Young Scientist Award in 2019 for his research work. He is an active reviewer of IET Communications, IEEE Wireless Comm. Letters, Journal of Information Technology (Springer), Personal and Ubiquitous Computing (Springer), Multimedia Tools and Applications (Springer), International Journal of Computer Applications in Technology (Inderscience), International Journal of Communication Systems (Wiley), Journal of The Institution of Engineers (India): Series B, Journal of Intelligent and Fuzzy Systems.

Cheng-Chi Lee

Dr. Lee received his PhD degree in Computer Science from National Chung Hsing University (NCHU), Taiwan, in 2007. He is currently a distinguished professor with the Department of Library and Information Science at Fu Jen Catholic University. Dr. Lee is currently an editorial board member of the International Journal of Network Security, Journal of Computer Science, Cryptography, International Journal of Internet Technology and Secured Transactions, and Electronics. He also served as a reviewer for many SCI-index journals, other journals, and other conferences. His current research interests include data security, cryptography, network security, mobile communications and computing, and wireless communications. Dr. Lee has published over 200 scientific articles on the above research fields in international journals and conferences. He is a member of IEEE, the Chinese Cryptology and Information Security Association (CCISA), the Library Association of The Republic of China, and the ROC Phi Tau Phi Scholastic Honor Society.

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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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