Secure Collaborative Sensing for Crowd Sourcing Spectrum Data in White Space Networks

Abstract

Collaborative Sensing is an important enabling technique for realizing opportunistic spectrum access in white space (cognitive radio) networks. We consider the security ramifications of crowdsourcing of spectrum sensing in presence of malicious users that report false measurements. We propose viewing the area of interest as a grid of square cells and using it to identify and disregard false measurements. The proposed mechanism is based on identifying outlier measurements inside each cell, as well as corroboration among neighboring cells in a hierarchical structure to identify cells with significant number of malicious nodes. We provide a framework for taking into consideration inherent uncertainties, such as loss due to distance and shadowing, to reduce the likelihood of inaccurate classification of legitimate measurements as outliers. We use simulations to evaluate the effectiveness of the proposed approach against attackers with varying degrees of sophistication. The results show that depending on the attacker-type and location parameters, in the worst case we can nullify the effect of up to 41% of attacker nodes in a particular region. This figure is as high as 100% for a large subset of scenarios.This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the IEEE DySPAN 2010 proceedings 978-1-4244-5188-3/10/$26.00 ©2010 IEEE

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“…Malady and Silva [23] propose a centralized soft-decision approach employing a rule that computes a weighted sum of the signal strengths reported by the sensing SUs, again with the channel being considered busy if the computed value is greater than a particular threshold. Fatemieh, Chandra, and Gunter [25] discuss that the usage of a statistical median provides results that are more robust to excessively high or low reports by malicious or deeply faded nodes than when using a mean value. In their proposal, they jointly employ both estimators in order to achieve a mix of accuracy (mean) and robustness (median) in the decision process.…”

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“…Such techniques only work if a minority of nodes in a cell are compromised. There has also been efforts to detect cells that are dominated by attackers [9]. The authors consider the averages of neighboring cells in a hierarchy and detect cells with outlying averages as attacker-dominated.…”

“…Much of the existing work aims to detect malicious nodes by identifying their measurements as abnormal or outlying [9], [11], [16]; if a majority of nodes in a given cell provide a reading in a common range, the other nodes may be identified as outliers. Such techniques only work if a minority of nodes in a cell are compromised.…”

“…We consider the problem of dealing with malicious nodes that report such data inaccurately, with specific application to AMI. In the proposed architecture, the white space service provider needs to collect spectrum sensing data from multiple sources such as smart meters, mobile units, or equipment on consumer premises (this process is also known as crowdsourcing [9]). These devices have varying or unknown degrees of integrity and risk of compromise.…”

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“…First, their effectiveness is limited by relying solely on sensor data for inferring (dis)trust. Second, they assume limits on the penetration of attackers in an area; attackers either constitute a small fraction of nodes in a small local neighborhood [30], or if they control the majority of nodes in a neighborhood, the preponderance of adjacent neighborhoods must be un-compromised [18].…”