Communication networks in power systems are a major part of the smart grid paradigm. It enables and facilitates the automation of power grid operation as well as self-healing in contingencies. Such dependencies on communication networks, though, create a roam for cyber-threats. An adversary can launch an attack on the communication network, which in turn reflects on power grid operation. Attacks could be in the form of false data injection into system measurements, flooding the communication channels with unnecessary data, or intercepting messages. Using machine learning-based processing on data gathered from communication networks and the power grid is a promising solution for detecting cyber threats. In this paper, a co-simulation of cyber-security for cross-layer strategy is presented. The advantage of such a framework is the augmentation of valuable data that enhances the detection as well as identification of anomalies in the operation of the power grid. The framework is implemented on the IEEE 118-bus system. The system is constructed in Mininet to simulate a communication network and obtain data for analysis. A distributed three controller software-defined networking (SDN) framework is proposed that utilizes the Open Network Operating System (ONOS) cluster. According to the findings of our suggested architecture, it outperforms a single SDN controller framework by a factor of more than ten times the throughput. This provides for a higher flow of data throughout the network while decreasing congestion caused by a single controller’s processing restrictions. Furthermore, our CECD-AS approach outperforms state-of-the-art physics and machine learning-based techniques in terms of attack classification. The performance of the framework is investigated under various types of communication attacks.
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A Framework for Joint Attack Detection and Control Under False Data Injection
In this work, we consider an LTI system with a Kalman filter,
detector, and Linear Quadratic Gaussian (LQG) controller under false
data injection attack. The interaction between the controller and adversary
is captured by a Stackelberg game, in which the controller is the
leader and the adversary is the follower. We propose a framework under
which the system chooses time-varying detection thresholds to reduce
the effectiveness of the attack and enhance the control performance. We
model the impact of the detector as a switching signal, resulting in a
switched linear system. A closed form solution for the optimal attack
is first computed using the proposed framework, as the best response
to any detection threshold. We then present a convex program to compute
the optimal detection threshold. Our approach is evaluated using a
numerical case study.
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- Award ID(s):
- 1656981
- NSF-PAR ID:
- 10131727
- Date Published:
- Journal Name:
- IEEE Conference on Decision and Game Theory for Security
- Page Range / eLocation ID:
- 352-363
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/https://doi.org/10.1007/978-3-030-32430-8_21
