Bank of Models: Sensor Attack Detection and Isolation in Industrial Control Systems

2021 ◽  
pp. 3-23
Author(s):  
Chuadhry Mujeeb Ahmed ◽  
Jianying Zhou
Keyword(s):  
Control Systems ◽  
Attack Detection ◽  
Industrial Control ◽  
Industrial Control Systems

scholarly journals PRECYSE: Cyber-attack Detection and Response for Industrial Control Systems

2014 ◽  
Author(s):  
Kieran McLaughlin ◽  
Sakir Sezer ◽  
Paul R. Smith ◽  
Zhendong Ma ◽  
Florian Skopik
Keyword(s):  
Control Systems ◽  
Attack Detection ◽  
Cyber Attack ◽  
Industrial Control ◽  
Industrial Control Systems

Cyber attack detection and mitigation: Software Defined Survivable Industrial Control Systems

2019 ◽  
Vol 25 ◽  
pp. 152-168 ◽  
Author(s):  
Hunor Sándor ◽  
Béla Genge ◽  
Zoltán Szántó ◽  
Lőrinc Márton ◽  
Piroska Haller
Keyword(s):  
Control Systems ◽  
Attack Detection ◽  
Cyber Attack ◽  
Industrial Control ◽  
Industrial Control Systems

scholarly journals FALCON: Framework for Anomaly Detection in Industrial Control Systems

Electronics ◽  
2020 ◽  
Vol 9 (8) ◽  
pp. 1192 ◽  
Author(s):  
Subin Sapkota ◽  
A K M Nuhil Mehdy ◽  
Stephen Reese ◽  
Hoda Mehrpouyan
Keyword(s):  
Water Treatment ◽  
Anomaly Detection ◽  
Control Systems ◽  
Short Term Memory ◽  
Critical Infrastructure ◽  
Attack Detection ◽  
Support Vector ◽  
Physical Processes ◽  
Industrial Control ◽  
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.

scholarly journals A Dual-Isolation-Forests-Based Attack Detection Framework for Industrial Control Systems

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 36639-36651 ◽  
Author(s):  
Mariam Elnour ◽  
Nader Meskin ◽  
Khaled Khan ◽  
Raj Jain
Keyword(s):  
Control Systems ◽  
Attack Detection ◽  
Industrial Control ◽  
Industrial Control Systems
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