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

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.

AutoML-ID: automated machine learning model for intrusion detection using wireless sensor network

“…It reduces the confusion of selecting the best-performing algorithm by automating the process. Further, it also overcomes the limitation of the LT-FS-ID algorithm 22 . LT-FS-ID algorithm only works if the input predictors are a positive real number.…”

“…It indicates that these models highly overestimate the number of required k -barriers. We have also compared the performance of the AutoML with previous studies 21 , 22 for the prediction of k -barriers and k -barrier coverage probability (Table 4 ).…”

“…Further, we also tested the performance of the AutoML approach over the publicly available intrusion detection dataset 22 . In a recent study, Singh et al 22 have proposed a log-transformed feature scaling based algorithm (i.e., LT-FS-ID) for intrusion detection considering uniform node distribution scenario. We downloaded the datasets and applied the proposed AutoML approach to them.…”

“…They found that the non-standardise methods accurately map the k -barrier coverage probability using the synthetic variables with R = 0.85 and RMSE = 0.095. More recently, Singh et al 22 proposed a logarithmic predictor transformation and scaling-based algorithm coupled with SVR (i.e., LT-FS-ID) to map the number of required k -barriers for fast intrusion detection and prevention over a rectangular Region of Interest (RoI) considering uniform sensor distribution. The dimension of the dataset LT-FS-ID is 182 5.…”

An Explainable Evolving Fuzzy Neural Network to Predict the k Barriers for Intrusion Detection Using a Wireless Sensor Network

“…This study uses a dataset obtained synthetically through Monte-Carlo simulations. The method for generating the data set as well as a detailed description of the generation process is described in [ 46 ]. The data set has five columns.…”