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

Abstract

Momentous increase in the popularity of explainable machine learning models coupled with the dramatic increase in the use of synthetic data facilitates us to develop a cost-efficient machine learning model for fast intrusion detection and prevention at frontier areas using Wireless Sensor Networks (WSNs). The performance of any explainable machine learning model is driven by its hyperparameters. Several approaches have been developed and implemented successfully for optimising or tuning these hyperparameters for skillful predictions. However, the major drawback of these techniques, including the manual selection of the optimal hyperparameters, is that they depend highly on the problem and demand application-specific expertise. In this paper, we introduced Automated Machine Learning (AutoML) model to automatically select the machine learning model (among support vector regression, Gaussian process regression, binary decision tree, bagging ensemble learning, boosting ensemble learning, kernel regression, and linear regression model) and to automate the hyperparameters optimisation for accurate prediction of numbers of k-barriers for fast intrusion detection and prevention using Bayesian optimisation. To do so, we extracted four synthetic predictors, namely, area of the region, sensing range of the sensor, transmission range of the sensor, and the number of sensors using Monte Carlo simulation. We used 80% of the datasets to train the models and the remaining 20% for testing the performance of the trained model. We found that the Gaussian process regression performs prodigiously and outperforms all the other considered explainable machine learning models with correlation coefficient (R = 1), root mean square error (RMSE = 0.007), and bias = − 0.006. Further, we also tested the AutoML performance on a publicly available intrusion dataset, and we observed a similar performance. This study will help the researchers accurately predict the required number of k-barriers for fast intrusion detection and prevention.

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

“…Recent works using this data set investigated solutions for the same problem using automated machine learning (AutoML) [ 14 ]. However, knowledge extraction on the data set in question was not presented so far.…”

“…Border protection interests all nations, especially those involved in armed conflicts or close to areas with a high flow of illegal products (drugs, weapons smuggling, and illegal immigrants, among others) [ 13 ]. To help control this identification of possible invaders, several sensors are used to identify suspicious movements at the borders of countries [ 14 ] ( Figure 1 ). This technology is relatively inexpensive and can help monitor existing activities in these locations.…”

“…This technology is relatively inexpensive and can help monitor existing activities in these locations. These sensors use machine-learning concepts to facilitate the identification of adverse situations [ 14 ]. This type of technology also helps to reduce the number of human resources guarding countries with large borders.…”

A Review of AIoT-based Edge Devices and Lightweight Deployment

“…Currently, there is a lot of mature research on how to design/search DNNs [162]. So we won't talk about it here.…”

“…SOTA AI models are developed and publicly available, achieving top performance. With the rapid development of technologies such as automated machine learning (AutoML) [162], which can build deep learning models automatically, including network structure design, hyperparameter tuning, etc. However, when people deploy AI models on practical applications, how to make AI more reliable is a big challenge [170].…”