Lightweight AI-Driven intrusion detection for SMEs: Comparative analysis of machine lerning models on Ton_Iot

Abstract

Small and medium-sized enterprises (SMEs) face escalating cyber threats and often lack the technical and financial capacity to deploy resource-intensive security solutions. This study evaluates the effectiveness of Artificial Intelligence (AI) and Machine Learning (ML) models for lightweight intrusion detection systems (IDS) tailored to SME environments. Six supervised algorithms including Logistic Regression, Naïve Bayes, Random Forest, Multi-Layer Perceptron (MLP), LightGBM, and XGBoost, were trained and tested on the TON_IoT dataset. Performance was assessed using standard metrics (accuracy, precision, recall, F1-score, ROC-AUC) alongside computational efficiency indicators (training time, inference latency, and model size). Results show that tree-based ensemble models, particularly XGBoost (accuracy = 99.07%, ROC-AUC = 0.9996) and LightGBM (accuracy = 99.00%, ROC-AUC = 0.9993), achieved superior detection performance while maintaining minimal computational cost. In contrast, Logistic Regression and Naïve Bayes exhibited faster training but lower detection accuracy (<76%). These findings confirm that AI-driven ensemble models can deliver both accuracy and efficiency, making them ideal for deployment in resource-constrained SMEs. Limitations of this study include reliance on a single dataset and supervised frameworks. Future work will extend to cross-dataset validation and real-world SME applications to enhance robustness and generalizability.