As part of Industrial Control Systems (ICS), the control logic controls the physical processes of critical infrastructures such as power plants and water and gas distribution. The Programmable Logic Controller (PLC) commonly manages these processes through actuators based on information received from sensor readings. Therefore, boundary checking is essential in ICS because sensor readings and actuator values must be within the safe range to ensure safe and secure ICS operation. In this paper, we propose an ontology-based approach to provide the knowledge required to verify the boundaries of ICS components with respect to their safety and security specifications. For the proof of concept, the formal model of the Programmable Logic Controller (PLC) is created in UPPAAL and validated in UPPAAL-API. Then, the proposed boundary verification algorithm is used to import the required information from the safety/security ontology
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FALCON: Framework for Anomaly Detection in Industrial Control Systems
Industrial Control Systems (ICS) are used to control physical processes in critical infrastructure. These systems are used in a wide variety of operations such as water treatment, power generation and distribution, and manufacturing. While the safety and security of these systems are of serious concern, recent reports have shown an increase in targeted attacks aimed at manipulating physical processes to cause catastrophic consequences. This trend emphasizes the need for algorithms and tools that provide resilient and smart attack detection mechanisms to protect ICS. In this paper, we propose an anomaly detection framework for ICS based on a deep neural network. The proposed methodology uses dilated convolution and long short-term memory (LSTM) layers to learn temporal as well as long term dependencies within sensor and actuator data in an ICS. The sensor/actuator data are passed through a unique feature engineering pipeline where wavelet transformation is applied to the sensor signals to extract features that are fed into the model. Additionally, this paper explores four variations of supervised deep learning models, as well as an unsupervised support vector machine (SVM) model for this problem. The proposed framework is validated on Secure Water Treatment testbed results. This framework detects more attacks in a shorter period of time than previously published methods.
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- Award ID(s):
- 1846493
- NSF-PAR ID:
- 10222650
- Date Published:
- Journal Name:
- Electronics
- Volume:
- 9
- Issue:
- 8
- ISSN:
- 2079-9292
- Page Range / eLocation ID:
- 1192
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/electronics9081192
