Next Article in Journal
Passive Round-Trip-Time Positioning in Dense IEEE 802.11 Networks
Next Article in Special Issue
Detection of Malicious Primary User Emulation Based on a Support Vector Machine for a Mobile Cognitive Radio Network Using Software-Defined Radio
Previous Article in Journal
Innovative Reconfigurable Metasurface 2-D Beam-Steerable Reflector for 5G Wireless Communication
Previous Article in Special Issue
Measuring the Impact of Accurate Feature Selection on the Performance of RBM in Comparison to State of the Art Machine Learning Algorithms

FALCON: Framework for Anomaly Detection in Industrial Control Systems

Department of Computer Science, College of Engineering, Boise State University, Boise, ID 83725, USA
Idaho National Laboratory, Idaho Falls, ID 83401, USA
Author to whom correspondence should be addressed.
Electronics 2020, 9(8), 1192;
Received: 28 June 2020 / Revised: 15 July 2020 / Accepted: 21 July 2020 / Published: 24 July 2020
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. View Full-Text
Keywords: industrial controls systems; attack detection; neural networks industrial controls systems; attack detection; neural networks
Show Figures

Figure 1

MDPI and ACS Style

Sapkota, S.; Mehdy, A.K.M.N.; Reese, S.; Mehrpouyan, H. FALCON: Framework for Anomaly Detection in Industrial Control Systems. Electronics 2020, 9, 1192.

AMA Style

Sapkota S, Mehdy AKMN, Reese S, Mehrpouyan H. FALCON: Framework for Anomaly Detection in Industrial Control Systems. Electronics. 2020; 9(8):1192.

Chicago/Turabian Style

Sapkota, Subin, A K M Nuhil Mehdy, Stephen Reese, and Hoda Mehrpouyan. 2020. "FALCON: Framework for Anomaly Detection in Industrial Control Systems" Electronics 9, no. 8: 1192.

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Back to TopTop