Catch Me if You Can: An Abnormality Detection Approach for Collaborative Spectrum Sensing in Cognitive Radio Networks

Li, Husheng; Han, Zhu

doi:10.1109/twc.2010.091510.100315

Cited by 124 publications

(95 citation statements)

References 26 publications

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“…In [14], the authors convert the area of interest into a grid of square cells and use it to identify and discard the outlier measurements. In [15], an approach based on abnormality detection in data mining is proposed. In [16], the authors present an abnormality detection scheme by using the path-loss exponent in signal propagation.…”

Section: Related Workmentioning

confidence: 99%

Defending Against Cooperative Attacks in Cooperative Spectrum Sensing

Qin

Hsieh

2013

IEEE Trans. Wireless Commun.

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Abstract-Accurate spectrum sensing is important in cognitive radio networks. False sensing results in either waste of spectrum or harmful interference to primary users. To improve accuracy, cooperative spectrum sensing, in which a set of secondary users cooperatively sense the presence of the primary user, has emerged. This technique, however, opens a window for malicious users and attackers, who may remotely or physically capture the sensors and manipulate the sensing reports. In this paper, we consider the attack model whereby the attacker injects selfconsistent false data simultaneously, and propose a modified COI (combinatorial optimization identification) algorithm to defend against such attacks. We also provide a theorem that detection uncertainty may exist in cooperative spectrum sensing. We intensively evaluate our algorithm with simulations, and the results show that our algorithm is a good technique to complement an existing algorithm, called IRIS.

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Section: Related Workmentioning

confidence: 99%

Defending Against Cooperative Attacks in Cooperative Spectrum Sensing

Qin

Hsieh

2013

IEEE Trans. Wireless Commun.

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“…But the challenges of cooperative sensing mainly include: the network architecture of centralized or distributed cooperation sensing [43]; detection fusion including decision fusion or data fusion [44,45]; and cooperative node selection. Furthermore, researches in [46,47] have improved the cooperation sensing performance from the space diversity and the abnormality detection perspectives.…”

Section: Spectrum Sensingmentioning

confidence: 99%

Intelligent and efficient development of wireless networks: A review of cognitive radio networks

et al. 2012

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Facing the challenges on how to improve spectrum efficiency and how to realize heterogeneous network convergence in future wireless networks, a cognitive radio network (CRN) is proposed as one of the solutions. This has become a major research topic in recent years and it is timely to give an overview of the development of CRN and to summarize key issues and technologies. The fundamental concepts of CRN, including the cognitive cycle model, the network architecture, and the cognitive ability and intelligent decision functions, are introduced in detail based on recent advances. Key issues for each topic, followed with recent research on theory and method, are then classified and the industrialization developments of CRN testbeds based on TD-LTE cellular system and standards are briefly presented. Finally, conclusions are reached on the perspectives and directions of future development.cognitive radio networks, network architecture, cognitive ability, spectrum sensing, intelligent decision and dynamic spectrum allocation, end-to-end performance Driven by the requirements of ubiquitous wireless access and personalized wireless applications [1], worldwide, numerous wireless technologies are invading our lives, such as evolutionary telecommunication technologies, wideband wireless access and short distance wireless technologies. Furthermore, the concept of the internet of things (IoT) has become widespread based on the development of wireless sensor networks. With the explosive development of wireless technologies, several challenges must be addressed. The electromagnetic spectrum, whose use is licensed and managed by government, has been scarce for allocation to ubiquitous wireless applications. However, contrary to the physical scarcity of the available spectrum, the report published by the federal communication commission (FCC) shows that over 60 % of the licensed spectrum below 6 GHz remains underused [2,3]. The measurement results in Figure 1 taken in Beijing over a one month period, also indicate low spectrum use consistent with the results released by the FCC. Therefore, improvement of spectrum efficiency is the first challenge to be examined. On the other hand, the end-to-end performance of wireless networks is considered to be the performance indicator for ubiquitous and personalized wireless applications [4], such as end-to-end throughput, end-to-end delay and routing scheme, which take into account all network elements in a data transmission flow across different layers for the global optimization from the network aspect. Because the property of heterogeneity causes low radio resource use and mutual interference among heterogeneous networks, the other big challenge is that heterogeneous network convergence is needed to improve the end-to-end performance of wireless networks.Cognitive radio (CR), proposed by Mitola in 1999 [5,6], is considered a feasible solution for improving spectrum efficiency. Furthermore, a cognitive radio network (CRN), which is defined as a wireless network with the capabilities

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“…For example, statistics-based anomaly detection has been proposed [12], [14], [17], [18]. Chen et al [12] proposed a sensor reputation management framework that assigns different weights to sensing reports based on their reputation achieved from a previous sensing history.…”

Section: Related Workmentioning

confidence: 99%

Robust cooperative sensing via state estimation in cognitive radio networks

Min

Kim²,

Shin

2011

2011 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN)

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Abstract-Cooperative sensing, a key enabling technology for dynamic spectrum access, is vulnerable to various sensingtargeted attacks, such as the primary user emulation or spectrum sensing data falsification. These attacks can easily disrupt the primary signal detection process, thus crippling the operation of dynamic spectrum access. While such sensing-targeted attacks can be easily launched by an attacker, it is very challenging to design a robust cooperative spectrum sensing scheme due mainly to the practical constraints inherent in spectrum sensing, particularly the shared/open nature of the wireless medium and the unpredictability of signal propagation. In this paper, we develop an efficient, yet simple attack detection framework, called IRIS (robust cooperatIve sensing via iteRatIve State estimation), that safeguards the incumbent detection process by checking the consistency among sensing reports via the estimation of system states, namely, the primary user's transmit-power and pathloss exponent. The key insight behind the design of IRIS is that the sensing results are governed by the network topology and the law of signal propagation, which cannot be easily compromised by an attacker. Consequently, the sensing reports must demonstrate consistency among themselves in estimating system states. Our analytical and simulation results show that, by performing consistency-checks, IRIS provides high attackdetection capability, and preserves satisfactory performance in estimating the system states even under very challenging attack scenarios. Based on these observations, we propose a new incumbent detection rule that can further improve the spectrum efficiency. IRIS can be readily deployed in infrastructure-based cognitive radio networks, such as IEEE 802.22 WRANs, with manageable processing and communication overheads.

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Catch Me if You Can: An Abnormality Detection Approach for Collaborative Spectrum Sensing in Cognitive Radio Networks

Cited by 124 publications

References 26 publications

Defending Against Cooperative Attacks in Cooperative Spectrum Sensing

Defending Against Cooperative Attacks in Cooperative Spectrum Sensing

Intelligent and efficient development of wireless networks: A review of cognitive radio networks

Robust cooperative sensing via state estimation in cognitive radio networks

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