Adaptive learning of Byzantines' behavior in cooperative spectrum sensing

Vempaty, Aditya; Agrawal, K.; Varshney, Pramod K.; Chen, Hao

doi:10.1109/wcnc.2011.5779320

Cited by 57 publications

(47 citation statements)

References 8 publications

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“…One can achieve this system in practice if there is an underlying scheme as proposed by [5] or [6] in the network, which lets the FC identify the Byzantine nodes. The nodes which are tagged honest, are later informed to employ SR through some feedback mechanism, while the nodes that are tagged Byzantine are left ignorant.…”

Section: ) Case-1 (Sr Employed Only At the Honest Nodes)mentioning

confidence: 99%

“…Note that this scheme works only when the percentage of Byzantines in the network is less than 50%. On the other hand, the adaptive learning scheme proposed by Vempaty et al in [6] works for any fraction of Byzantines in the network. They learnt the operating points of each and every node in the inference network not only to identify the Byzantines, but also to use the learnt Byzantine parameters in an adaptive fusion rule in order to improve the detection performance over Rawat et al's scheme [5].…”

Section: Introductionmentioning

confidence: 99%

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On noise-enhanced distributed inference in the presence of Byzantines

Gagrani

Sharma

Iyengar

et al. 2011

2011 49th Annual Allerton Conference on Communication, Control, and Computing (Allerton)

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Abstract-This paper considers the noise-enhanced distributed detection problem in the presence of Byzantine (malicious) nodes by suitably adding stochastic resonance (SR) noise. We consider two metrics -the minimum number of Byzantines (α blind ) needed to blind the fusion center as a security metric and the KullbackLeibler divergence (DKL) as a detection performance metric. We show that α blind increases when SR noise is added at the honest nodes. When Byzantines also start adding SR noise to their observations, we see no gain in terms of α blind . However, the detection performance of the network does improve with SR.We also consider a game theoretic formulation where this problem of distributed detection in the presence of Byzantines is modeled as a minimax game between the Byzantines and the inference network, and numerically find Nash equilibria.The case when SR noise is added to the signals received at the fusion center (FC) from the sensors is also considered. Our numerical results indicate that while there is no gain in terms of α blind , the network-wide performance measured in terms of the deflection coefficient does improve in this case.

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Section: ) Case-1 (Sr Employed Only At the Honest Nodes)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

On noise-enhanced distributed inference in the presence of Byzantines

Gagrani

Sharma

Iyengar

et al. 2011

2011 49th Annual Allerton Conference on Communication, Control, and Computing (Allerton)

Self Cite

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show abstract

“…While Byzantine attacks (originally proposed in [10]) may, in general, refer to many types of malicious behavior, our focus in this paper is on data-falsification attacks [11]- [18]. Thus far, research on detection in the presence of Byzantine attacks has predominantly focused on addressing these attacks under the centralized April 15, 2015 DRAFT model [13], [14], [18], [19]. A few attempts have been made to address the security threats in the distributed or consensus based schemes in recent research [20]- [25].…”

Section: Introductionmentioning

confidence: 99%

“…As shown in our earlier work [18] in the context of distributed detection with one-bit measurements under a centralized model, an intelligent way to improve the performance of the network is to use the information of the identified Byzantines to the network's benefit. More specifically, learning based techniques have the potential to outperform the existing exclusion based techniques.…”

Section: Introductionmentioning

confidence: 99%

Data Falsification Attacks on Consensus-Based Detection Systems

Kailkhura

Brahma

Varshney

2017

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

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This paper considers the problem of detection in distributed networks in the presence of data falsification (Byzantine) attacks. Detection approaches considered in the paper are based on fully distributed consensus algorithms, where all of the nodes exchange information only with their neighbors in the absence of a fusion center. In such networks, we characterize the negative effect of Byzantines on the steady-state and transient detection performance of the conventional consensus based detection algorithms. To address this issue, we study the problem from the network designer's perspective. More specifically, we first propose a distributed weighted average consensus algorithm that is robust to Byzantine attacks. We show that, under reasonable assumptions, the global test statistic for detection can be computed locally at each node using our proposed consensus algorithm. We exploit the statistical distribution of the nodes' data to devise techniques for mitigating the influence of data falsifying Byzantines on the distributed detection system. Since some parameters of the statistical distribution of the nodes' data might not be known a priori, we propose learning based techniques to enable an adaptive design of the local fusion or update rules.

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“…In order to mitigate the attack, the FC removes nodes which are tagged Byzantine node from the fusion rule. Another mitigation scheme was proposed by Vempaty et al [21], where each sensor's behavior is learnt over time and compared to the known behavior of the honest nodes. Any significant deviation in the learnt behavior from the expected honest behavior is labelled Byzantine node.…”

Section: Introductionmentioning

confidence: 99%

Distributed Inference With M-Ary Quantized Data in the Presence of Byzantine Attacks

Nadendla

Han

Varshney

2014

IEEE Trans. Signal Process.

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Abstract-The problem of distributed inference with M-ary quantized data at the sensors is investigated in the presence of Byzantine attacks. We assume that the attacker does not have knowledge about either the true state of the phenomenon of interest, or the quantization thresholds used at the sensors. Therefore, the Byzantine nodes attack the inference network by modifying modifying the symbol corresponding to the quantized data to one of the other M symbols in the quantization alphabetset and transmitting the false symbol to the fusion center (FC). In this paper, we find the optimal Byzantine attack that blinds any distributed inference network. As the quantization alphabet size increases, a tremendous improvement in the security performance of the distributed inference network is observed.We also investigate the problem of distributed inference in the presence of resource-constrained Byzantine attacks. In particular, we focus our attention on two problems: distributed detection and distributed estimation, when the Byzantine attacker employs a highly-symmetric attack. For both the problems, we find the optimal attack strategies employed by the attacker to maximally degrade the performance of the inference network. A reputationbased scheme for identifying malicious nodes is also presented as the network's strategy to mitigate the impact of Byzantine threats on the inference performance of the distributed sensor network.

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Adaptive learning of Byzantines' behavior in cooperative spectrum sensing

Cited by 57 publications

References 8 publications

On noise-enhanced distributed inference in the presence of Byzantines

On noise-enhanced distributed inference in the presence of Byzantines

Data Falsification Attacks on Consensus-Based Detection Systems

Distributed Inference With M-Ary Quantized Data in the Presence of Byzantine Attacks

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