Tachyon: Enhancing stacked models using Bayesian optimization for intrusion detection using different sampling approaches

Abstract

The integration of sensors in the monitoring of essential bodily measurements, air quality, and energy consumption in buildings demonstrates the importance of the Internet of Things (IoT) in everyday life. These security breaches are caused by rudimentary and immature security protocols that are implemented on IoT devices. An intrusion detection system is used to detect security threats and system-level applications to detect malicious activities. This paper introduces Tachyon, a combination of various statistical and tree-based Artificial Intelligence (AI) techniques, such as Extreme Gradient Boosting (XGBoost), Random Forest (RF), Bidirectional Auto-Regressive Transformers (BART), Logistic Regression (LR), Multivariate Adaptive Regression Splines (MARS), Decision Tree (DT), and a top k stack ensemble to distinguish between normal and malicious attacks in a binary classification setting. The IoTID2020 dataset used in this study consists of 6,25,783 samples with 83 features. An initial examination of the data reveals its unbalanced nature. To create a balanced dataset, a range of sampling techniques were used, including Oversampling, Undersampling, Synthetic Minority Oversampling Technique (SMOTE), Random Oversampling Examples (ROSE), Borderline Synthetic Minority Oversampling Technique (b-SMOTE), and Adaptive Synthetic (ADASYN). In addition, principal component analysis (PCA) and partial least squares (PLS) were used to determine the most significant features. The experimental results demonstrate that the stacked ensemble achieved a performance of 99.8%, which is better than the baseline approaches. An ablation study of ensemble models was also conducted to assess the performance of the proposed model in various scenarios.