Cost-benefit analysis of moving-target defense in power grids

We study moving-target defense (MTD) that actively perturbs transmission line reactances to thwart stealthy false data injection (FDI) attacks against state estimation in a power grid. Prior work on this topic has proposed MTD based on randomly selected reactance perturbations, but these perturbations cannot guarantee effective attack detection. To address the issue, we present formal design criteria to select MTD reactance perturbations that are truly effective. However, based on a key optimal power flow (OPF) formulation, we find that the effective MTD may incur a non-trivial operational cost that has not hitherto received attention. Accordingly, we characterize important tradeoffs between the MTD's detection capability and its associated required cost. Extensive simulations, using the MATPOWER simulator and benchmark IEEE bus systems, verify and illustrate the proposed design approach that for the first time addresses both key aspects of cost and effectiveness of the MTD

Cost-Benefit Analysis of Moving-Target Defense in Power Grids

We study moving-target defense (MTD) that actively perturbs transmission line reactances to thwart stealthy false data injection (FDI) attacks against state estimation in a power grid. Prior work on this topic has proposed MTD based on randomly selected reactance perturbations, but these perturbations cannot guarantee effective attack detection. To address the issue, we present formal design criteria to select MTD reactance perturbations that are truly effective. However, based on a key optimal power flow (OPF) formulation, we find that the effective MTD may incur a non-trivial operational cost that has not hitherto received attention. Accordingly, we characterize important tradeoffs between the MTD's detection capability and its associated required cost. Extensive simulations, using the MATPOWER simulator and benchmark IEEE bus systems, verify and illustrate the proposed design approach that for the first time addresses both key aspects of cost and effectiveness of the MTD.Comment: IEEE/IFIP International Conference on Dependable Systems and Networks (DSN) - 201

Improving the resilience of cyber-physical systems under strategic adversaries

Renewable energy resources challenge traditional energy system operations by substituting the stability and predictability of fossil fuel based generation with the unreliability and uncertainty of wind and solar power. Rising demand for green energy drives grid operators to integrate sensors, smart meters, and distributed control to compensate for this uncertainty and improve the operational efficiency of the grid. Real-time negotiations enable producers and consumers to adjust power loads during shortage periods, such as an unexpected outage or weather event, and to adapt to time-varying energy needs. While such systems improve grid performance, practical implementation challenges can derail the operation of these distributed cyber-physical systems. Network disruptions introduce instability into control feedback systems, and strategic adversaries can manipulate power markets for financial gain. This dissertation analyzes the impact of these outages and adversaries on cyber-physical systems and provides methods for improving resilience, with an emphasis on distributed energy systems. First, a financial model of an interdependent energy market lays the groundwork for profit-oriented attacks and defenses, and a game theoretic strategy optimizes attack plans and defensive investments in energy systems with multiple independent actors. Then attacks and defenses are translated from a theoretical context to a real-time energy market via denial of service (DoS) outages and moving target defenses. Analysis on two market mechanisms shows how adversaries can disrupt market operation, destabilize negotiations, and extract profits by attacking network links and disrupting communication. Finally, a low-cost DoS defense technique demonstrates a method that energy systems may use to defend against attacks

Digital Twins for Moving Target Defense Validation in AC Microgrids

Cyber-physical microgrids are vulnerable to stealth attacks that can degrade their stability and operability by performing low-magnitude manipulations in a coordinated manner. This paper formulates the interactions between CSAs and microgrid defenders as a non-cooperative, zero-sum game. Additionally, it presents a hybrid Moving Target Defense (MTD) strategy for distributed microgrids that can dynamically alter local control gains to achieve resiliency against Coordinated Stealth Attacks (CSAs). The proposed strategy reduces the success probability of attack(s) by making system dynamics less predictable. The framework also identifies and removes malicious injections by modifying secondary control weights assigned to them. The manipulated signals are reconstructed using an Artificial Neural Network (ANN)-based Digital Twin (DT) to preserve stability. To guarantee additional immunity against instability arising from gain alterations, MTD decisions are also validated (via utility and best response computations) using the DT before actual implementation. The DT is also used to find the minimum perturbation that defenders must achieve to invalidate an attacker's knowledge effectively.Comment: IEEE Energy Conversion Congress and Expo (ECCE) 202

Defense by Deception against Stealthy Attacks in Power Grids

Cyber-physical Systems (CPSs) and the Internet of Things (IoT) are converging towards a hybrid platform that is becoming ubiquitous in all modern infrastructures. The integration of the complex and heterogeneous systems creates enormous space for the adversaries to get into the network and inject cleverly crafted false data into measurements, misleading the control center to make erroneous decisions. Besides, the attacker can make a critical part of the system unavailable by compromising the sensor data availability. To obfuscate and mislead the attackers, we propose DDAF, a deceptive data acquisition framework for CPSs\u27 hierarchical communication network. Each switch in the hierarchical communication network generates a random pattern of addresses/IDs by shuffling the original sensor IDs reported through it. During the data acquisition from remotely located sensors to the central controller, the switches craft the network packets by replacing a few sensors\u27 associated addresses/IDs with the generated deceptive IDs and by adding decoy data for the rest. While misleading the attackers, the control center must retrieve the actual data to operate the system correctly. We propose three remapping mechanisms (e.g., seed-based, prediction-based, and hybrid) and compare their robustness against different stealthy attacks. Due to the deception, artfully altered measurements turn into random data injections, making it easy to remove them as outliers. As the outliers and the estimated residuals contain the potential attack vectors, DDAF can detect and localize the attack points and the targeted sensors by analyzing this information. DDAF is generic and scalable to be implemented in any hierarchical CPSs network. Experimental results on the standard IEEE 14, 57, and 300 bus power systems show that DDAF can detect, mitigate, and localize up-to 100% of the stealthy cyberattacks. To the best of our knowledge, this is the first framework that implements complete randomization in the data acquisition of the hierarchical CPSs

A Comprehensive Survey on the Cyber-Security of Smart Grids: Cyber-Attacks, Detection, Countermeasure Techniques, and Future Directions

One of the significant challenges that smart grid networks face is cyber-security. Several studies have been conducted to highlight those security challenges. However, the majority of these surveys classify attacks based on the security requirements, confidentiality, integrity, and availability, without taking into consideration the accountability requirement. In addition, some of these surveys focused on the Transmission Control Protocol/Internet Protocol (TCP/IP) model, which does not differentiate between the application, session, and presentation and the data link and physical layers of the Open System Interconnection (OSI) model. In this survey paper, we provide a classification of attacks based on the OSI model and discuss in more detail the cyber-attacks that can target the different layers of smart grid networks communication. We also propose new classifications for the detection and countermeasure techniques and describe existing techniques under each category. Finally, we discuss challenges and future research directions