V. Sriram Siddhardh Nadendla

Secure distributed detection in the presence of eavesdroppers

We investigate the structure of quantizer rules at the local sensors in distributed detection networks, in the presence of eavesdroppers (Eve), under asymptotic regime (number of sensors tending to infinity) for binary hypotheses. These local quantizers are designed in such a way that the confidentiality of sensor data is preserved while achieving optimal detection performance at the fusion center (FC). In the case of Eve with noisier channels, for a general channel model, we show that these optimal quantizer rules at the local sensors are always on the boundaries of the achievable region of sensors ROC. If there is a constraint on the Eves performance, based on our numerical results, we conjecture that the structure of an optimal quantizer is LRT-based. The above argument is corroborated with a numerical example using BSC channels for the Eve and ideal channels for the FC. In the case of Eve with better channels, we prove that the quantizer rules that can provide confidentiality along with optimal detection performance, cannot send any useful information to the fusion center (FC). We propose a jamming scheme for the FC against Eve and evaluate the optimal distribution for the Gaussian jamming signal that requires minimum energy to make both FC and Eves channel similar in distributed detection performance.

On noise-enhanced distributed inference in the presence of Byzantines

The problem of Byzantine (malicious sensors) threats in a distributed detection framework for inference networks is addressed. Impact of Byzantines is mitigated by suitably adding Stochastic Resonance (SR) noise. Previously, Independent Malicious Byzantine Attack (IMBA), where each Byzantine decides to attack the network independently relying on its own observation was considered. In this paper, we present further results for Cooperative Malicious Byzantine Attack (CMBA), where Byzantines collaborate to make the decision and use this information for the attack. In order to analyze the network performance, we consider KL-Divergence (KLD) to quantify detection performance and minimum fraction of Byzantines needed to blind the network (αblind) as a security metric. We show that both KLD and αblind increase when SR noise is added at the honest sensors. When SR noise is added to the fusion center, we analytically show that there is no gain in terms of αblind or the network-wide performance measured in terms of the deflection coefficient. We also model a game between the network and the Byzantines and present a necessary condition for a strategy (SR noise) to be a saddle-point equilibrium.

Distributed inference in the presence of eavesdroppers: a survey

The distributed inference framework comprises a group of spatially distributed nodes that acquire observations about a POI and transmit computed summary statistics to the fusion center. Based on the messages received from the nodes, the FC makes a global inference about the POI. The distributed and broadcast nature of such systems makes them quite vulnerable to different types of attacks. This article focuses on efficient mitigation schemes to mitigate the impact of eavesdropping on distributed inference and surveys the currently available approaches along with avenues for future research.

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

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 Byzantine nodes attack the inference network by modifying the symbol corresponding to the quantized data to one of the other symbols in the quantization alphabet-set and transmitting falsified 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. In addition to the perfect channel case, in Appendix A , we also analyze the optimal Byzantine attack when the channel between the nodes and the FC is noisy and is modelled as a discrete M-ary channel. We also investigate the optimal attack within the restricted space of highly-symmetric attack strategies, that maximally degrades the performance of the inference network in the presence of resource-constrained Byzantine attacks. A reputation-based scheme for identifying malicious nodes is also presented as the networks strategy to mitigate the impact of Byzantine threats on the inference performance of the distributed sensor network. We also provide asymptotic analysis to find the optimal reputation-based scheme as a function of the fraction of compromised nodes in the network.

Design of Binary Quantizers for Distributed Detection Under Secrecy Constraints

In this paper, we investigate the design of distributed detection networks in the presence of an eavesdropper (Eve). We consider the problem of designing binary sensor quantizers that maximize the Kullback-Leibler (KL) divergence at the fusion center (FC), when subject to a tolerable constraint on the KL divergence at Eve. We assume that the channels between the sensors and the FC (likewise the channels between the sensors and the Eve) are modeled as binary symmetric channels (BSCs). In the case of i.i.d. received symbols at both the FC and Eve, we prove that the structure of the optimal binary quantizers is a likelihood ratio test (LRT). We also present an algorithm to find the threshold of the optimal LRT, and illustrate it for the case of Additive white Gaussian noise (AWGN) observation models at the sensors. In the case of non-i.i.d. received symbols at both FC and Eve, we propose a dynamic-programming based algorithm to find efficient quantizers at the sensors. Numerical results are presented to illustrate the performance of the proposed network.

An auction-based mechanism for dynamic spectrum allocation in participatory cognitive radio networks

The problem of dynamic spectrum allocation for participatory cognitive radio (CR) networks is modeled using an auction-based mechanism, where the fusion center (FC) acts as an auctioneer and allocates spectrum to CRs without complete knowledge regarding spectrum availability. We also consider the cost of collisions with the primary user (PU) and assign this cost to the FC, making it completely responsible for its allocation decision. With the help of CRs participating in the network, the FC makes a global inference on the availability of the spectrum followed by spectrum allocation. The goal of this paper is to investigate the design of an optimal auction-based framework for participatory CR networks, and to find the conditions under which a CR actively participates in the optimal auction (or, collaborative spectrum sensing). We also identify a scenario in the optimal auction design where the FC pays to the participating CRs, in order to improve the sensing performance, while simultaneously maximizing its revenue.

Minimax games for cooperative spectrum sensing in a centralized cognitive radio network in the presence of interferers

In this paper, we consider the problem of interferers for cooperative spectrum sensing in a centralized cognitive radio network comprising N cognitive radios (CRs) and one fusion center (FC) in the presence of a fixed interferer. The design metric chosen is the error probability. We prove the existence of a saddle-point in the minimax game between the interferer and the CR network. An optimal solution is found that maximizes the objective with respect to the interferers parameters and minimizes the same with respect to the CR networks parameters. We show that the probability of error is a quasi-convex function with respect to the networks parameters and a monotone function with respect to the interferers parameters. We also present numerical results that corroborate our theoretical results.

Optimal Spectrum Auction Design With 2-D Truthful Revelations Under Uncertain Spectrum Availability

In this paper, we propose a novel sealed-bid auction framework to address the problem of dynamic spectrum allocation in cognitive radio (CR) networks. We design an optimal auction mechanism that maximizes the moderator’s expected utility, when the spectrum is not available with certainty. We assume that the moderator employs collaborative spectrum sensing in order to make a reliable inference about spectrum availability. Due to the presence of a collision cost whenever the moderator makes an erroneous inference, and a sensing cost at each CR, we investigate feasibility conditions that guarantee a non-negative utility at the moderator. Since the moderator fuses CRs’ sensing decisions to obtain a global inference regarding spectrum availability, we propose a novel strategy-proof fusion rule that encourages the CRs to simultaneously reveal truthful sensing decisions, along with truthful valuations to the moderator. We also present tight theoretical bounds on instantaneous network throughput achieved by our auction mechanism. Numerical examples are presented to provide insights into the performance of the proposed auction under different scenarios.

On ARQ-based wireless communication systems in the presence of a strategic jammer

We investigate the design and performance of ARQ-based systems for wireless point-to-point (P2P) communication links with perfect feedback channels in the presence of a strategic jammer over an additive white Gaussian noise channel subject to InterSymbol Interference (ISI). We define system-latency as the number of transmission attempts at the transmitter to achieve a successful transfer of a data packet to the receiver. We attempt to minimize it by modeling this as a constrained optimization problem where the system-latency is minimized such that the probability of successfully receiving a data packet at the receiver satisfies a prescribed guarantee. A game-theoretic formulation is provided. Numerical results are presented for illustration purposes.

On jamming models against collaborative spectrum sensing in a simple cognitive radio network

We design the optimal jamming attack strategy for a cognitive radio network in the presence of path-loss decaying signal models. We consider a cognitive radio network with K participating cognitive radios and one fusion center in the presence of one primary user and one jammer in the operating region. We assume that the network is not aware of the presence of the jammer and hence employs the optimal decision rules designed for a benign environment. Jammer, on the other hand, tries to take advantage of this ignorance and carries the best possible attack so that it can maximally deteriorate the global performance (error-probability) of the network under a total power-constraint. We consider a two-fold attack - one on the CR (sensor) reception and other on the fusion center reception. We present numerical results depicting near-field and far-field effects over different path-loss exponents to find the optimal jamming attack for a relatively simple example where the network has only one sensor (K = 1). This example serves as an illustration of the basic concepts and will be followed by a more in-depth study.