A deep learning approach to predict the number of k-barriers

MATLAB code for "A deep learning approach to predict the number of k-barriers for intrusion detection over a circular region using WSNs."

This file contains the MATLAB code for "A deep learning approach to predict the number of k-barriers for intrusion detection over a circular region using WSNs, 2022, Expert Systems with Applications."

For more information please refer to the following link;

https://doi.org/10.1016/j.eswa.2022.118588

If you need a full-text of this manuscript then please email to me ([e-mail]) or you can request it through ResearchGate.

If you are using this code then please cite the following paper;

Singh, A., Amutha, J., Nagar, J., & Sharma, S. (2022). A deep learning approach to predict the number of k-barriers for intrusion detection over a circular region using wireless sensor networks. Expert Systems with Applications, 118588.

Additional references for further reading;

1. Singh, A., Nagar, J., Sharma, S., & Kotiyal, V. (2021). A Gaussian process regression approach to predict the k-barrier coverage probability for intrusion detection in wireless sensor networks. Expert Systems with Applications, 172, 114603. https://doi.org/10.1016/j.eswa.2021.114603
2. Singh, A., Amutha, J., Nagar, J., Sharma, S., & Lee, C. C. (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(3), 1070. https://doi.org/10.3390/s22031070
3. Singh, A., Amutha, J., Nagar, J., Sharma, S., & Lee, C. C. (2022). AutoML-ID: automated machine learning model for intrusion detection using wireless sensor network. Scientific Reports, 12(1), 1-14. https://www.nature.com/articles/s41598-022-13061-z