Generating Synthetic Dataset for ML-Based IDS Using CTGAN and Feature Selection to Protect Smart IoT Environmentsopen access
- Authors
- Alabdulwahab, Saleh; Kim, Young-Tak; Seo, Aria; Son, Yunsik
- Issue Date
- Oct-2023
- Publisher
- MDPI
- Keywords
- intrusion detection system; machine learning; information security; IoT; CTGAN; advanced persistent threat
- Citation
- Applied Sciences-basel, v.13, no.19
- Indexed
- SCIE
SCOPUS
- Journal Title
- Applied Sciences-basel
- Volume
- 13
- Number
- 19
- URI
- https://scholarworks.korea.ac.kr/kumedicine/handle/2021.sw.kumedicine/64313
- DOI
- 10.3390/app131910951
- ISSN
- 2076-3417
- Abstract
- Networks within the Internet of Things (IoT) have some of the most targeted devices due to their lightweight design and the sensitive data exchanged through smart city networks. One way to protect a system from an attack is to use machine learning (ML)-based intrusion detection systems (IDSs), significantly improving classification tasks. Training ML algorithms require a large network traffic dataset; however, large storage and months of recording are required to capture the attacks, which is costly for IoT environments. This study proposes an ML pipeline using the conditional tabular generative adversarial network (CTGAN) model to generate a synthetic dataset. Then, the synthetic dataset was evaluated using several types of statistical and ML metrics. Using a decision tree, the accuracy of the generated dataset reached 0.99, and its lower complexity reached 0.05 s training and 0.004 s test times. The results show that synthetic data accurately reflect real data and are less complex, making them suitable for IoT environments and smart city applications. Thus, the generated synthetic dataset can further train models to secure IoT networks and applications.
- Files in This Item
- There are no files associated with this item.
- Appears in
Collections - 4. Research institute > Research Institute of Molecular Medicine and Nutrition > 1. Journal Articles
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.