Trees Bootstrap Aggregation for Detection and Characterization of IoT-SCADA Network Traffic
- Authors
- Ahakonye, Love Allen Chijioke; Nwakanma, Cosmas Ifeanyi; Lee, Jae-Min; Kim, Dong-Seong
- Issue Date
- Nov-2023
- Publisher
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- Keywords
- Protocols; Monitoring; Industrial Internet of Things; Security; SCADA systems; Encryption; Standards; Bootstrap aggregation; decision trees; ensemble; IEC-60870-5-104 protocol; industrial Internet of Things (IIoT); Internet of Things supervisory control and data acquisition (IoT-SCADA); machine learning (ML); network communication
- Citation
- IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
- Journal Title
- IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
- URI
- https://scholarworks.bwise.kr/kumoh/handle/2020.sw.kumoh/26478
- DOI
- 10.1109/TII.2023.3333438
- ISSN
- 1551-3203
1941-0050
- Abstract
- The accelerated industrial transformation has witnessed the supervisory control and data acquisition (SCADA) transit from monolithic to the Internet of Things (IoT-SCADA). The development also transformed conventional specialized serial-based to transmission control protocol/internet protocol reliant standard communication protocols, such as IEC-60870-5-104 (IEC-104), thereby increasing vulnerability to attacks and intrusions. Maintaining the reliability and availability of IoT-SCADA demands versatile and robust monitoring of network traffic. This study proposes a monitoring technique to detect and characterize the IEC-104 IoT-SCADA network traffic. The proposed trees bootstrap aggregation monitoring technique of GridSearchCV() hyperparameter tuning of 11 n-estimator, 20 max-depth, and 5-k cross-validation achieved early detection and characterization. Experimental results demonstrate its sensitivity and precision in detecting and classifying various network traffic and application types at a minimal execution time while reducing false alarm rates, which is vital for mitigating intrusions in heterogeneous IoT-SCADA networks.
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