This study explores the development of robust Intrusion Detection Systems (IDS) to enhance cybersecurity in Wireless Sensor Networks (WSNs) within the evolving Internet of Things (IoT) ecosystem. It leverages a publicly available dataset derived from UNSW-NB15, retrieved from a GitHub repository, capturing diverse network traffic attributes (dttl, swin, dwin, tcprtt, synack, ackdat), protocol-specific indicators (proto tcp, proto udp), and service-specific attributes (service dns). These features enable precise analysis of TCP/IP headers and traffic patterns, supporting multi-class classification into four categories: Analysis, Denial of Service (DoS), Exploits, and Normal. Advanced machine learning algorithms, including Random Forest, Support Vector Machines (SVM), and K-Nearest Neighbors (KNN), were applied with systematic preprocessing (including KNN-based imputation, normalization, and one-hot encoding), feature selection using Random Forest importance, and 5-fold cross-validation. The best performance was achieved by Random Forest (accuracy, precision, recall, and F1-score of 99.9877%), followed by KNN (99.9754%) and SVM (99.9630%). The study demonstrates that combining well-structured models with relevant protocol-level features and robust evaluation strategies can significantly enhance intrusion detection capabilities in IoT-based environments. It reinforces the value of using modern public datasets and interpretable algorithms for building scalable and reliable IDS solutions.
Lmkaiti et al. (Wed,) studied this question.