A power grid is a complex system connecting electric power generators to consumers through power transmission and distribution networks across a large geographical area. System monitoring is necessary to ensure the reliable operation of power grids, and state estimation is used in system monitoring to best estimate the power grid state through analysis of meter measurements and power system models. Various techniques have been developed to detect and identify bad measurements, including interacting bad measurements introduced by arbitrary, nonrandom causes. At first glance, it seems that these techniques can also defeat malicious measurements injected by attackers.In this article, we expose an unknown vulnerability of existing bad measurement detection algorithms by presenting and analyzing a new class of attacks, called false data injection attacks, against state estimation in electric power grids. Under the assumption that the attacker can access the current power system configuration information and manipulate the measurements of meters at physically protected locations such as substations, such attacks can introduce arbitrary errors into certain state variables without being detected by existing algorithms. Moreover, we look at two scenarios, where the attacker is either constrained to specific meters or limited in the resources required to compromise meters. We show that the attacker can systematically and efficiently construct attack vectors in both scenarios to change the results of state estimation in arbitrary ways. We also extend these attacks to generalized false data injection attacks, which can further increase the impact by exploiting measurement errors typically tolerated in state estimation. We demonstrate the success of these attacks through simulation using IEEE test systems, and also discuss the practicality of these attacks and the real-world constraints that limit their effectiveness.

False data injection attacks against state estimation in electric power grids

A power grid is a complex system connecting electric power generators to consumers through power transmission and distribution networks across a large geographical area. System monitoring is necessary to ensure the reliable operation of power grids, and state estimation is used in system monitoring to best estimate the power grid state through analysis of meter measurements and power system models. Various techniques have been developed to detect and identify bad measurements, including the interacting bad measurements introduced by arbitrary, non-random causes. At first glance, it seems that these techniques can also defeat malicious measurements injected by attackers.In this paper, we present a new class of attacks, called false data injection attacks, against state estimation in electric power grids. We show that an attacker can exploit the configuration of a power system to launch such attacks to successfully introduce arbitrary errors into certain state variables while bypassing existing techniques for bad measurement detection. Moreover, we look at two realistic attack scenarios, in which the attacker is either constrained to some specific meters (due to the physical protection of the meters), or limited in the resources required to compromise meters. We show that the attacker can systematically and efficiently construct attack vectors in both scenarios, which can not only change the results of state estimation, but also modify the results in arbitrary ways. We demonstrate the success of these attacks through simulation using IEEE test systems. Our results indicate that security protection of the electric power grid must be revisited when there are potentially malicious attacks.

The development of a trustworthy smart grid requires a deeper understanding of potential impacts resulting from successful cyber attacks. Estimating feasible attack impact requires an evaluation of the grid's dependency on its cyber infrastructure and its ability to tolerate potential failures. A further exploration of the cyber-physical relationships within the smart grid and a specific review of possible attack vectors is necessary to determine the adequacy of cybersecurity efforts. This paper highlights the significance of cyber infrastructure security in conjunction with power application security to prevent, mitigate, and tolerate cyber attacks. A layered approach is introduced to evaluating risk based on the security of both the physical power applications and the supporting cyber infrastructure. A classification is presented to highlight dependencies between the cyber-physical controls required to support the smart grid and the communication and computations that must be protected from cyber attack. The paper then presents current research efforts aimed at enhancing the smart grid's application and infrastructure security. Finally, current challenges are identified to facilitate future research efforts.

Cyber–Physical System Security for the Electric Power Grid

Computer algebra systems are now ubiquitous in all areas of science and engineering. This highly successful textbook, widely regarded as the “bible of computer algebra”, gives a thorough introduction to the algorithmic basis of the mathematical engine in computer algebra systems. Designed to accompany oneor two-semester courses for advanced undergraduate or graduate students in computer science or mathematics, its comprehensiveness and reliability has also made it an essential reference for professionals in the area. Special features include: detailed study of algorithms including time analysis; implementation reports on several topics; complete proofs of the mathematical underpinnings; and a wide variety of applications (among others, in chemistry, coding theory, cryptography, computational logic, and the design of calendars and musical scales). A great deal of historical information and illustration enlivens the text. In this third edition, errors have been corrected and much of the Fast Euclidean Algorithm chapter has been renovated.

Modern computer algebra

It is often appealing to assume that existing solutions can be directly applied to emerging engineering domains. Unfortunately, careful investigation of the unique challenges presented by new domains exposes its idiosyncrasies, thus often requiring new approaches and solutions. In this paper, we argue that the “smart” grid, replacing its incredibly successful and reliable predecessor, poses a series of new security challenges, among others, that require novel approaches to the field of cyber security. We will call this new field cyber-physical security. The tight coupling between information and communication technologies and physical systems introduces new security concerns, requiring a rethinking of the commonly used objectives and methods. Existing security approaches are either inapplicable, not viable, insufficiently scalable, incompatible, or simply inadequate to address the challenges posed by highly complex environments such as the smart grid. A concerted effort by the entire industry, the research community, and the policy makers is required to achieve the vision of a secure smart grid infrastructure.

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Cyber–Physical Security of a Smart Grid Infrastructure

This paper considers the two-user Gaussian arbitrarily-varying broadcast channel, wherein a power-limited transmitter wishes to send a message to each of two receivers. Each receiver sees a superposition of the transmitter's sequence, Gaussian noise, and a signal from a power-limited malicious jammer. The jammer is assumed to know the code, but is oblivious to real-time transmissions. The exact capacity region of this setting is determined to be the capacity region of the standard Gaussian broadcast channel, but with the noise variance increased by the power of the jammer, as long as the received power of the jammer at each receiver is less than that of the legitimate transmitter. A key aspect of the achievable scheme involves sharing randomness from the transmitter to the receivers by breaking the transmitted sequence into segments, and either transmitting at full power in a segment, or sending zero. By coding over the on/off signal, a small shared randomness can be established without corruption by the jammer, and without interfering with the standard superposition coding strategy for the Gaussian broadcast channel.

Capacity Region of the Gaussian Arbitrarily-Varying Broadcast Channel

The distributed source coding problem is considered when the sensors, or encoders, are under Byzantine attack; that is, an unknown number of sensors have been reprogrammed by a malicious intruder to undermine the reconstruction at the fusion center. Three different forms of the problem are considered. The first is a variable-rate setup, in which the decoder adaptively chooses the rates at which the sensors transmit. An explicit characterization of the variable-rate minimum achievable sum rate is stated, given by the maximum entropy over the set of distributions indistinguishable from the true source distribution by the decoder. In addition, two forms of the fixed-rate problem are considered, one with deterministic coding and one with randomized coding. The achievable rate regions are given for both these problems, with a larger region achievable using randomized coding, though both are suboptimal compared to variable-rate coding.

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Variable-Rate Distributed Source Coding in the Presence of Byzantine Sensors

The second-order converse bound of multiple access channels is an intriguing problem in information theory. In this work, in the setting of the two-user discrete memoryless multiple access channel (DM-MAC) under the maximal error probability criterion, we investigate the gap between the best achievable rates and the asymptotic capacity region. With “wringing techniques” and meta-converse arguments, we show that gap at blocklength $n$ is upper bounded by $O(1/\sqrt{n})$ .

A Wringing-Based Proof of a Second-Order Converse for the Multiple-Access Channel under Maximal Error Probability

A wide range of information technology applications require the identification of a particular message or label that represents the identity of an object at a distance, e.g., over a wireless channel. Conventionally, the underlying information that represents the identity is transmitted over the channel, following the information-theoretic concept of message transmission. If the purpose of the interaction over the channel is only to verify (match) an identity, then the concept of identification over channels—utilizing the identification codes that have been developed by the information theory community—can provide an exponential efficiency gain over message transmission. This topical review article conducts for the first time a comprehensive detailed evaluation of the existing identification codes for the practically relevant regime of finite parameters. We examine essentially all published identification codes, including codes based on inner constant weight codes that are concatenated with outer linear block codes, such as Reed-Solomon and Reed-Muller codes. Specifically, we conduct a holistic identification code comparison based on the logarithm of the number of representable identities (in shannon), the size (in bit) of the transmitted cue that represents an identity, and the corresponding type II error probability bound for essentially all existing identification codes. Based on the resulting insights, we formulate guidelines for the design of practical (finite-parameter) identification codes. For instance, we find that a linear block code (without concatenation with a sophisticated inner constant-weight code) is sufficient for most practical identification code usages.

Identification Codes: A Topical Review With Design Guidelines for Practical Systems

We introduce a gain function viewpoint of information leakage by proposing maximal g -leakage , a rich class of operationally meaningful leakage measures that subsumes recently introduced leakage measures maximal leakage and maximal α -leakage. In maximal g -leakage, the gain of an adversary in guessing an unknown random variable is measured using a gain function applied to the probability of correctly guessing. In particular, maximal g -leakage captures the multiplicative increase, upon observing Y , in the expected gain of an adversary in guessing a randomized function of X , maximized over all such randomized functions. We show that maximal leakage is an upper bound on maximal g -leakage, for any non-negative gain function g . We obtain a closed-form expression for maximal g -leakage for a class of concave gain functions. We also study two variants of maximal g -leakage depending on the type of an adversary and obtain closed-form expressions for them, which do not depend on the particular gain function considered as long as it satisﬁes some mild regularity conditions. We do this by developing a variational characterization for the R´enyi divergence of order inﬁnity which naturally generalizes the deﬁnition of pointwise maximal leakage to incorporate arbitrary gain functions. Finally, we study information leakage in the scenario where an adversary can make multiple guesses by focusing on maximizing a speciﬁc gain function related to α -loss. In particular, we ﬁrst completely characterize the minimal expected α -loss under multiple guesses and analyze how the corresponding leakage measure is affected with the number of guesses. We also show that a new measure of divergence that belongs to the class of Bregman divergences captures the relative performance of an arbitrary adversarial strategy with respect to an optimal strategy in minimizing the expected α -loss. leakage R´enyi divergence of orders. the setting which attempts in guessing by focusing on a speciﬁc tunable gain function. Such a has for log-loss (a special case of our tunable loss function) which extensively used machine There studied. We maximal g -leakage is equal to Sibson mutual information of order class of concave gain We new measure of divergence relative arbitrary adversarial strategy with minimizing One limitation of our that we reverse

Oliver Kosut

Papers

Capacity Region of the Gaussian Arbitrarily-Varying Broadcast Channel

Variable-Rate Distributed Source Coding in the Presence of Byzantine Sensors

A Wringing-Based Proof of a Second-Order Converse for the Multiple-Access Channel under Maximal Error Probability

Identification Codes: A Topical Review With Design Guidelines for Practical Systems

An Operational Approach to Information Leakage via Generalized Gain Functions