Distributed Inference with Byzantine Data: State-of-the-Art Review on Data Falsification Attacks

Vempaty, Aditya; Tong, Lang; Varshney, Pramod K.

doi:10.1109/msp.2013.2262116

Cited by 139 publications

(91 citation statements)

References 25 publications

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“…It has been shown in [84], [85] that for a distributed inference network consisting of malicious sensors [86], an appropriate addition of noise makes the system more robust to attacks and increases the minimum number of attacked sensors required to deteriorate the network's performance.…”

Section: ) Optimal Noise Distributionsmentioning

confidence: 99%

Noise-Enhanced Information Systems

Chen

Varshney

2014

Proc. IEEE

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Abstract-Noise, traditionally defined as an unwanted signal or disturbance, has been shown to play an important constructive role in many information processing systems and algorithms. This noise enhancement has been observed and employed in many physical, biological, and engineered systems. Indeed stochastic facilitation (SF) has been found critical for certain biological information functions like detection of weak, subthreshold stimuli or suprathreshold signals through both experimental verification and analytical model simulations.In this paper, we present a systematic noise-enhanced information processing framework to analyze and optimize the performance of engineered systems. System performance is evaluated not only in terms of signal-to-noise ratio but also in terms of other more relevant metrics such as probability of error for signal detection or mean square error for parameter estimation.As an important new instance of SF, we also discuss the constructive effect of noise in associative memory recall. Potential enhancement of image processing systems via the addition of noise is discussed with important applications in biomedical image enhancement, image denoising and classification.

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Section: ) Optimal Noise Distributionsmentioning

confidence: 99%

Noise-Enhanced Information Systems

Chen

Varshney

2014

Proc. IEEE

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“…In a second situation (decision fusion with corrupted nodes), the fusion center has to tackle with the presence of a number of malevolent nodes, which deliberately alter their reports to induce a decision error. According to a consolidated literature, such nodes are referred to as byzantine nodes or simply Byzantines [7], [8]. Note that a byzantine node can decide to alter its report by relying on its observations of the system, but usually it does not have access to the observations made by the other nodes and their reports 1 .…”

Section: Introductionmentioning

confidence: 99%

“…The last case (decision fusion with corrupted reports) corresponds to a situation in which the adversary corrupts the reports without having access to the observed sequences. This may correspond to a situation in which the adversary does not control the nodes but only the communication link between the nodes and the fusion center, or to the case of byzantine nodes which, for some reasons, can not observe the data at the input of the node, or decide not exploit such a knowledge (as strange as it may seem, this is a rather common assumption in the analysis of decision fusion in the presence of byzantine nodes [8]). …”

Section: Introductionmentioning

confidence: 99%

Decision fusion with corrupted reports in multi-sensor networks: A game-theoretic approach

Abrardo

Barni

Kallas

et al. 2014

53rd IEEE Conference on Decision and Control

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Decision fusion in adversarial setting is receiving increasing attention due to its relevance in several applications including sensor networks, cognitive radio, social networks, distributed network monitoring. In most cases, a fusion center has to make a decision based on the reports provided by local agents, e.g. the nodes of a multi-sensor network. In this paper, we consider a setup in which the fusion center makes its decision on the status of an observed system by relying on the decisions made by a pool of local nodes and by taking into account the possibility that some nodes maliciously corrupt their reports to induce a decision error. We do so by casting the problem into a game-theoretic framework and looking for the existence of an equilibrium point defining the optimum strategies for the fusion center and the malicious nodes. We analyze two different strategies for the fusion center: a strategy recently introduced by Varshney et al. in a cognitive radio setup and a new approach based on soft identification of malicious nodes. The superior performance of the new decision scheme are demonstrated by resorting to the game-theoretic framework introduced previously.

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“…Reference [23], in the context of cognitive radio (CR), proposed a prefiltering scheme of sensing data and a trust factor is assigned to each user to detect the malicious CR ones. The authors of [24], in the context of target localization, also consider binary Byzantine attacks where the SNs transmit to the FC their binary decisions and they propose two techniques to mitigate the compromised SNs negative impact on the FC decision. To mitigate the Byzantine effect on the data fusion problem in cooperative spectrum sensing, a weighted sequential probability ratio test was proposed in [25].…”

Section: Introductionmentioning

confidence: 99%

A Secure Optimum Distributed Detection Scheme in Under-Attack Wireless Sensor Networks

Nurellari

McLernon

Ghogho

2018

IEEE Trans. on Signal and Inf. Process. over Networks

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Abstract-We address the problem of centralized detection of a binary event in the presence of β fraction falsifiable sensor nodes (SNs) (i.e., controlled by an attacker) for a bandwidthconstrained under − attack spatially uncorrelated distributed wireless sensor network (WSN). The SNs send their one-bit test statistics over orthogonal channels to the fusion center (FC), which linearly combines them to reach to a final decision. Adopting the modified deflection coefficient as an alternative function to be optimized, we first derive in a closed-form the FC optimal weights combining. But as these optimal weights require a − priori knowledge that cannot be attained in practice, this optimal weighted linear FC rule is not implementable. We also derive in a closed-form the expressions for the attacker "flipping probability" (defined in paper) and the minimum fraction of compromised SNs that makes the FC incapable of detecting. Next, based on the insights gained from these expressions, we propose a novel and non-complex reliability-based strategy to identify the compromised SNs and then adapt the weights combining proportional to their assigned reliability metric. In this way, the FC identifies the compromised SNs and decreases their weights in order to reduce their contributions towards its final decision. Finally, simulation results illustrate that the proposed strategy significantly outperforms (in terms of FC's detection capability) the existing compromised SNs identification and mitigation schemes.

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Distributed Inference with Byzantine Data: State-of-the-Art Review on Data Falsification Attacks

Cited by 139 publications

References 25 publications

Noise-Enhanced Information Systems

Noise-Enhanced Information Systems

Decision fusion with corrupted reports in multi-sensor networks: A game-theoretic approach

A Secure Optimum Distributed Detection Scheme in Under-Attack Wireless Sensor Networks

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