GPS spoofing attack characterization and detection in smart grids

Pradhan, Parth; Nagananda, K. G.; Venkitasubramaniam, Parv; Kishore, Shalinee; Blum, Rick S.

doi:10.1109/cns.2016.7860525

Cited by 41 publications

(25 citation statements)

References 9 publications

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“…6) which proves ψ H h (λ; r 1:∞ ) ≤ ψ H h (λ;r), ∀λ ∈ |X |−1 . For the Legendre transforms the inequality is reversed, yielding Ψ H h (ω; r 1:∞ ) ≥ Ψ H h (ω;r), ∀ω ∈ P(X ).…”

mentioning

confidence: 64%

“…Modern networks are vulnerable to malicious attacks. For instance, the civilian global positioning system (GPS) is particularly exposed to spoofing attacks [4], which can impair wireless ad-hoc communication systems [5], or alter the timing information in smart grids [6], [7]. As a consequence, the timestamp information of the system may be altered to the point that the data arriving to a central decision unit can be considered unlabeled.…”

Section: A Related Workmentioning

confidence: 99%

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Algorithms and Fundamental Limits for Unlabeled Detection Using Types

Maranò

Willett

2019

IEEE Trans. Signal Process.

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Emerging applications of sensor networks for detection sometimes suggest that classical problems ought be revisited under new assumptions. This is the case of binary hypothesis testing with independent -but not necessarily identically distributedobservations under the two hypotheses, a formalism so orthodox that it is used as an opening example in many detection classes. However, let us insert a new element, and address an issue perhaps with impact on strategies to deal with "big data" applications: What would happen if the structure were streamlined such that data flowed freely throughout the system without provenance? How much information (for detection) is contained in the sample values, and how much in their labels? How should decision-making proceed in this case? The theoretical contribution of this work is to answer these questions by establishing the fundamental limits, in terms of error exponents, of the aforementioned binary hypothesis test with unlabeled observations drawn from a finite alphabet. Then, we focus on practical algorithms. A low-complexity detector -called ULR -solves the detection problem without attempting to estimate the labels. A modified version of the auction algorithm is then considered, and two new greedy algorithms with O(n 2 ) worst-case complexity are presented, where n is the number of observations. The detection operational characteristics of these detectors are investigated by computer experiments. Index TermsUnlabeled detection, fundamental limits of hypothesis testing, error exponents, types, assignment problem, greedy algorithms.S. Marano is with DIEM,

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confidence: 64%

Section: A Related Workmentioning

confidence: 99%

Algorithms and Fundamental Limits for Unlabeled Detection Using Types

Maranò

Willett

2019

IEEE Trans. Signal Process.

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“…noise drawn from the σ 2 w -variance distribution whose PDF is f w (x/σ w )/σ w and CDF is F w (x/σ w ), where f w (x) and F w (x) are the corresponding unit-variance PDF and CDF, and Q i (·) implies a binary quantizer which produces 1 if the argument is DRAFT larger than a scalar threshold τ i and 0 otherwise. The thresholds of N quantizers are identical given any time index 1 . We assume that the PDF f w (w) is log-concave, which is often met in practice such as Gaussian distributions, and the thresholds of N quantizers are identical given any time index.…”

Section: Problem Setupmentioning

confidence: 99%

“…DRAFT Then the variates p (1) , p (2) · · · , p (K) are distributed as K descending ordered statistics from an uniform…”

Section: Approximations On Permutation Matrix Recovery Probabilitymentioning

confidence: 99%

Signal Amplitude Estimation and Detection From Unlabeled Binary Quantized Samples

Wang

Zhu

Blum

et al. 2018

IEEE Trans. Signal Process.

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Signal amplitude estimation and detection from unlabeled quantized binary samples are studied, assuming that the order of the time indexes is completely unknown. First, maximum likelihood (ML) estimators are utilized to estimate both the permutation matrix and unknown signal amplitude under arbitrary, but known signal shape and quantizer thresholds. Sufficient conditions are provided under which an ML estimator can be found in polynomial time and an alternating maximization algorithm is proposed to solve the general problem via good initial estimates. In addition, the statistical identifiability of the model is studied. Furthermore, the generalized likelihood ratio test (GLRT) detector is adopted to detect the presence of signal. In addition, an accurate approximation to the probability of successful permutation matrix recovery is derived, and explicit expressions are provided to reveal the relationship between the number of signal samples and the number of quantizers. Finally, numerical simulations are performed to verify the theoretical results.

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“…Over a larger geographical area, cooperative authentication through pairwise checking by cross-correlation with encrypted P(Y) signal was used. Detection mechanisms for GNSS spoofing in power systems are also being explored in industry and academia [39]. Impact analysis of GNSS spoofing on power systems applications is also being researched [17].…”

Section: Current Efforts and Collaborative Opportunities Secure Amentioning

confidence: 99%

Timing Challenges in the Smart Grid

Allnutt¹,

Anand²,

Arnold³

et al. 2017

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Executive SummaryCorrect time and timing is one of the foundational elements in enabling the communication and orchestration of technologies for accurate and optimal wide area monitoring, protection and control (WAMPAC) in the power industry. The National Institute of Standards and Technology (NIST) and the International Electrical and Electronic Engineer -Standard Association (IEEE-SA) conducted a workshop to gather inputs from stakeholders to identify, analyze, and provide guidance on technologies, standards and methodologies for addressing the practical timing challenges that are currently being experienced in wide area time synchronization.One of the key drivers for the workshop is the need to prioritize NIST efforts in addressing precision timing needs in power systems. NIST has established a Smart Grid Testbed, which includes the Precision Timing module to support the IEEE 1588 Power Profile efforts and to serve as a basis for timing measurement and security research. The objective of the workshop was to identify the timing challenges, the community of experts, and potential collaborators as well as key research priorities to guide future efforts to ensuring that the integrity, availability, accuracy, and precision of timing requirements are met in power systems.Precision timing synchronization with requirements ranging from one microsecond to hundreds of nanoseconds within and across substations is currently being characterized and monitored by utilities in order to advance capabilities in real-time measurement and control and to reap the economic and environmental benefits of more intelligent and efficient use of generation and storage resources while meeting customer demands. The proliferation of widely deployed smart sensors for WAMPAC, distribution and energy management systems, along with the increasing need for fault detection and location as well as maintaining system stability in real time using spatio-temporal and temporalfrequency analyses, all require precision timing. These new capabilities offer increased flexibility to grid operators, but they also raise new security concerns.Among the concerns expressed include timing discontinuities such as leap seconds, Daylight Savings Time, and GPS Week Number Rollovers. Additionally, the increasing prevalence of jamming and the potential for spoofing is leading security, time and network experts in power systems or other domains to explore alternative propagation methods of traceable time. These include eLOng-RAnge Navigation (eLORAN), Iridium, IEEE 1588 Precision Time Protocol (PTP), Network Time Protocol (NTP), radio (WWVB is the NIST operated time signal radio station near Fort Collins, Colorado.), atomic clocks, and other terrestrial and satellite-based timing propagation solutions.Given the current stage of exploration and characterization for real time control, utilities and system integrators identified the top industry challenge to be integrity assurance, with the first steps being device conformance and interoperability testing, performance mo...

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GPS spoofing attack characterization and detection in smart grids

Cited by 41 publications

References 9 publications

Algorithms and Fundamental Limits for Unlabeled Detection Using Types

Algorithms and Fundamental Limits for Unlabeled Detection Using Types

Signal Amplitude Estimation and Detection From Unlabeled Binary Quantized Samples

Timing Challenges in the Smart Grid

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