288 research outputs found
Smart Grid Security: Threats, Challenges, and Solutions
The cyber-physical nature of the smart grid has rendered it vulnerable to a
multitude of attacks that can occur at its communication, networking, and
physical entry points. Such cyber-physical attacks can have detrimental effects
on the operation of the grid as exemplified by the recent attack which caused a
blackout of the Ukranian power grid. Thus, to properly secure the smart grid,
it is of utmost importance to: a) understand its underlying vulnerabilities and
associated threats, b) quantify their effects, and c) devise appropriate
security solutions. In this paper, the key threats targeting the smart grid are
first exposed while assessing their effects on the operation and stability of
the grid. Then, the challenges involved in understanding these attacks and
devising defense strategies against them are identified. Potential solution
approaches that can help mitigate these threats are then discussed. Last, a
number of mathematical tools that can help in analyzing and implementing
security solutions are introduced. As such, this paper will provide the first
comprehensive overview on smart grid security
Modeling and Detecting False Data Injection Attacks against Railway Traction Power Systems
Modern urban railways extensively use computerized sensing and control
technologies to achieve safe, reliable, and well-timed operations. However, the
use of these technologies may provide a convenient leverage to cyber-attackers
who have bypassed the air gaps and aim at causing safety incidents and service
disruptions. In this paper, we study false data injection (FDI) attacks against
railways' traction power systems (TPSes). Specifically, we analyze two types of
FDI attacks on the train-borne voltage, current, and position sensor
measurements - which we call efficiency attack and safety attack -- that (i)
maximize the system's total power consumption and (ii) mislead trains' local
voltages to exceed given safety-critical thresholds, respectively. To
counteract, we develop a global attack detection (GAD) system that serializes a
bad data detector and a novel secondary attack detector designed based on
unique TPS characteristics. With intact position data of trains, our detection
system can effectively detect the FDI attacks on trains' voltage and current
measurements even if the attacker has full and accurate knowledge of the TPS,
attack detection, and real-time system state. In particular, the GAD system
features an adaptive mechanism that ensures low false positive and negative
rates in detecting the attacks under noisy system measurements. Extensive
simulations driven by realistic running profiles of trains verify that a TPS
setup is vulnerable to the FDI attacks, but these attacks can be detected
effectively by the proposed GAD while ensuring a low false positive rate.Comment: IEEE/IFIP DSN-2016 and ACM Trans. on Cyber-Physical System
Comprehensive Survey and Taxonomies of False Injection Attacks in Smart Grid: Attack Models, Targets, and Impacts
Smart Grid has rapidly transformed the centrally controlled power system into
a massively interconnected cyber-physical system that benefits from the
revolutions happening in the communications (e.g. 5G) and the growing
proliferation of the Internet of Things devices (such as smart metres and
intelligent electronic devices). While the convergence of a significant number
of cyber-physical elements has enabled the Smart Grid to be far more efficient
and competitive in addressing the growing global energy challenges, it has also
introduced a large number of vulnerabilities culminating in violations of data
availability, integrity, and confidentiality. Recently, false data injection
(FDI) has become one of the most critical cyberattacks, and appears to be a
focal point of interest for both research and industry. To this end, this paper
presents a comprehensive review in the recent advances of the FDI attacks, with
particular emphasis on 1) adversarial models, 2) attack targets, and 3) impacts
in the Smart Grid infrastructure. This review paper aims to provide a thorough
understanding of the incumbent threats affecting the entire spectrum of the
Smart Grid. Related literature are analysed and compared in terms of their
theoretical and practical implications to the Smart Grid cybersecurity. In
conclusion, a range of technical limitations of existing false data attack
research is identified, and a number of future research directions is
recommended.Comment: Double-column of 24 pages, prepared based on IEEE Transaction articl
Optimal Attack against Cyber-Physical Control Systems with Reactive Attack Mitigation
This paper studies the performance and resilience of a cyber-physical control
system (CPCS) with attack detection and reactive attack mitigation. It
addresses the problem of deriving an optimal sequence of false data injection
attacks that maximizes the state estimation error of the system. The results
provide basic understanding about the limit of the attack impact. The design of
the optimal attack is based on a Markov decision process (MDP) formulation,
which is solved efficiently using the value iteration method. Using the
proposed framework, we quantify the effect of false positives and
mis-detections on the system performance, which can help the joint design of
the attack detection and mitigation. To demonstrate the use of the proposed
framework in a real-world CPCS, we consider the voltage control system of power
grids, and run extensive simulations using PowerWorld, a high-fidelity power
system simulator, to validate our analysis. The results show that by carefully
designing the attack sequence using our proposed approach, the attacker can
cause a large deviation of the bus voltages from the desired setpoint. Further,
the results verify the optimality of the derived attack sequence and show that,
to cause maximum impact, the attacker must carefully craft his attack to strike
a balance between the attack magnitude and stealthiness, due to the
simultaneous presence of attack detection and mitigation
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