Document Type
Journal Article
Publication Title
Sensors
Volume
24
Issue
12
PubMed ID
38931500
Publisher
MDPI
School
Centre for Securing Digital Futures
RAS ID
70347
Abstract
Cybersecurity has become a major concern in the modern world due to our heavy reliance on cyber systems. Advanced automated systems utilize many sensors for intelligent decision-making, and any malicious activity of these sensors could potentially lead to a system-wide collapse. To ensure safety and security, it is essential to have a reliable system that can automatically detect and prevent any malicious activity, and modern detection systems are created based on machine learning (ML) models. Most often, the dataset generated from the sensor node for detecting malicious activity is highly imbalanced because the Malicious class is significantly fewer than the Non-Malicious class. To address these issues, we proposed a hybrid data balancing technique in combination with a Cluster-based Under Sampling and Synthetic Minority Oversampling Technique (SMOTE). We have also proposed an ensemble machine learning model that outperforms other standard ML models, achieving 99.7% accuracy. Additionally, we have identified the critical features that pose security risks to the sensor nodes with extensive explainability analysis of our proposed machine learning model. In brief, we have explored a hybrid data balancing method, developed a robust ensemble machine learning model for detecting malicious sensor nodes, and conducted a thorough analysis of the model’s explainability.
DOI
10.3390/s24123712
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Comments
Zubair, M., Janicke, H., Mohsin, A., Maglaras, L., & Sarker, I. H. (2024). Automated sensor node malicious activity detection with explainability analysis. Sensors, 24(12), 3712. https://doi.org/10.3390/s24123712