Biblio

The operation of the modern power grid is becoming increasingly reliant on its underlying communication network, especially within the context of the rapidly growing integration of Distributed Energy Resources (DERs). This tight cyber-physical coupling brings uncertainties and challenges for the power grid operation and control. To help operators manage the complex cyber-physical environment, ensure the integrity, and continuity of reliable grid operation, a two-stage approach is proposed that is compatible with current ICS protocols to improve the deliverability of time critical operations. With the proposed framework, the impact Denial of Service (DoS) attack can have on a Transmission Control Protocol (TCP) session could be effectively prevented and mitigated. This coordinated approach combines the efficiency of congestion window reconfiguration and the applicability of physical-only mitigation approaches. By expanding the state and action space to encompass both the cyber and physical domains. This approach has been proven to outperform the traditional, physical-only method, in multiple network congested scenarios that were emulated in a real-time cyber-physical testbed.

Updating the structure of attack graph templates based on real-time alerts from Intrusion Detection Systems (IDS), in an Industrial Control System (ICS) network, is currently done manually by security experts. But, a highly-connected smart power systems, that can inadvertently expose numerous vulnerabilities to intruders for targeting grid resilience, needs automatic fast updates on learning attack graph structures, instead of manual intervention, to enable fast isolation of compromised network to secure the grid. Hence, in this work, we develop a technique to first construct a prior Bayesian Attack Graph (BAG) based on a predefined threat model and a synthetic communication network for a cyber-physical power system. Further, we evaluate a few score-based and constraint-based structural learning algorithms to update the BAG structure based on real-time alerts, based on scalability, data dependency, time complexity and accuracy criteria.

To ensure power system operational security, it not only requires security incident detection, but also automated intrusion response and recovery mechanisms to tolerate failures and maintain the system's functionalities. In this paper, we present a design procedure for remedial action schemes (RAS) that improves the power systems resiliency against accidental failures or malicious endeavors such as cyber attacks. A resilience-oriented optimal power flow is proposed, which optimizes the system security instead of the generation cost. To improve its speed for online application, a fast greedy algorithm is presented to narrow the search space. The proposed techniques are computationally efficient and are suitable for online RAS applications in large-scale power systems. To demonstrate the effectiveness of the proposed methods, there are two case studies with IEEE 24-bus and IEEE 118-bus systems.

Automatic optimal response systems are essential for preserving power system resilience and ensuring faster recovery from emergency under cyber compromise. Numerous research works have developed such response engine for cyber and physical system recovery separately. In this paper, we propose a novel cyber-physical decision support system, SCORE, that computes optimal actions considering pure and hybrid cyber-physical states, using Markov Decision Process (MDP). Such an automatic decision making engine can assist power system operators and network administrators to make a faster response to prevent cascading failures and attack escalation respectively. The hybrid nature of the engine makes the reward and state transition model of the MDP unique. Value iteration and policy iteration techniques are used to compute the optimal actions. Tests are performed on three and five substation power systems to recover from attacks that compromise relays to cause transmission line overflow. The paper also analyses the impact of reward and state transition model on computation. Corresponding results verify the efficacy of the proposed engine.