Affiliation:
1. Department of Computer Sciences and Engineering, College of Applied Science, King Saud University, Riyadh 11543, Saudi Arabia
2. Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia
Abstract
The prevalence of Internet of Things (IoT) technologies is on the rise, making the identification of anomalies in IoT systems crucial for ensuring their security and reliability. However, many existing approaches rely on static classifiers and immutable datasets, limiting their effectiveness. In this paper, we have utilized the UNSW-NB15 dataset, which contains 45 variables including multi- and binary-target variables, to determine the most relevant properties for detecting abnormalities in IoT systems. To address this issue, our research has investigated the use of active learning-based algorithms for anomaly detection in IoT systems. Active learning is a powerful technique that improves precision and productivity by eliminating the need for labeling and adapting to dynamic IoT environments. Additionally, our study has combined feature engineering methods, active learning approaches, and a random forest classifier to construct a resilient anomaly detection model for IoT devices. The proposed model has outperformed several state-of-the-art techniques, achieving an impressive accuracy rate of 99.7%. By implementing a rigorous sampling procedure and leveraging the collaborative nature of the random forest technique, our model has demonstrated a notable level of precision with a weighted average accuracy of 0.995. The findings of the study offered empirical evidence, supporting the efficacy of our active learning methodology in identifying abnormalities in IoT systems. Moreover, our study provides valuable insights and recommendations for future research and development activities in this field. Overall, this research contributes to the advancement of anomaly detection techniques in IoT systems, further enhancing their security and reliability.
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
Reference38 articles.
1. A comprehensive study of anomaly detection schemes in IoT networks using machine learning algorithms;Diro;Sensors,2021
2. Priya, V., Thaseen, I.S., Gadekallu, T.R., Aboudaif, M.K., and Nasr, E.A. (2021). Robust attack detection approach for IIoT using ensemble classifier. arXiv.
3. Efficient anomaly detection for smart hospital IoT systems;Said;Sensors,2021
4. A machine learning security framework for iot systems;Bagaa;IEEE Access,2020
5. Attack and anomaly detection in IoT networks using supervised machine learning approaches;Tyagi;Revue d’Intell. Artif.,2021