Malicious user suppression for cooperative spectrum sensing in cognitive radio networks using Dixon's outlier detection method

Kalamkar, Sanket S.; Banerjee, Adrish; Roychowdhury, Ananya

doi:10.1109/ncc.2012.6176787

Cited by 29 publications

(25 citation statements)

References 10 publications

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“…Anderson-Darling goodness-of-fit test is used in [18] to detect MUs by checking whether empirical distribution of SUs fit the expected distribution of a MU. In [19], the outlier…”

Section: B Summary Of Results and Related Workmentioning

confidence: 99%

Block Outlier Methods for Malicious User Detection in Cooperative Spectrum Sensing

Kalamkar

Singh

Banerjee

2014

2014 IEEE 79th Vehicular Technology Conference (VTC Spring)

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Block outlier detection methods, based on Tietjen-Moore (TM) and Shapiro-Wilk (SW) tests, are proposed to detect and suppress spectrum sensing data falsification (SSDF) attacks by malicious users in cooperative spectrum sensing. First, we consider basic and statistical SSDF attacks, where the malicious users attack independently. Then we propose a new SSDF attack, which involves cooperation among malicious users by masking. In practice, the number of malicious users is unknown. Thus, it is necessary to estimate the number of malicious users, which is found using clustering and largest gap method. However, we show using Monte Carlo simulations that, these methods fail to estimate the exact number of malicious users when they cooperate. To overcome this, we propose a modified largest gap method.Index Terms-Block outlier detection, cooperative spectrum sensing, malicious user, spectrum sensing data falsification attack.

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“…Anderson-Darling goodness-of-fit test is used in [18] to detect MUs by checking whether empirical distribution of SUs fit the expected distribution of a MU. In [19], the outlier…”

Section: B Summary Of Results and Related Workmentioning

confidence: 99%

Block Outlier Methods for Malicious User Detection in Cooperative Spectrum Sensing

Kalamkar

Singh

Banerjee

2014

2014 IEEE 79th Vehicular Technology Conference (VTC Spring)

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“…In this case, at FC, all of the SSDF attacks are considered as an outlier-detection problem, since the energy of the MSU is extremely higher/lower than that of the SU as assumed in [6][7][8]. Outlier detection is also a hypothesis testing problem where the null hypothesis is absence of any outlier and the alternative hypothesis is the presence of one or more outliers.…”

Section: Nonadaptive Algorithmsmentioning

confidence: 99%

“…The performance is compared using plots of additional probability of false alarm versus additional probability of missdetection. In [8], Grubb's methods I and II along with Dixon's test are analyzed, for "always yes" SSDF attack in order to remove MSUs. The authors have shown that Dixon's test performs better for removal a single MSU; however, it cannot remove more than one MSU.…”

Section: Introductionmentioning

confidence: 99%

Performance of p-Norm Detector in Cognitive Radio Networks with Cooperative Spectrum Sensing in Presence of Malicious Users

Lavanis¹,

Jalihal

2017

Wireless Communications and Mobile Computing

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A cognitive radio network (CRN) with a cooperative spectrum sensing scheme is considered. This CRN has a primary user and multiple secondary users, some of which are malicious secondary users (MSUs). Energy detection at each SU is performed using a p-norm detector with p≥2, where p=2 corresponds to the standard energy detector. The MSUs are capable of perpetrating spectrum sensing data falsification (SSDF) attacks. At the fusion center (FC), an algorithm is used to suppress these MSUs which could be either an adaptive weighing algorithm or one of the following: Tietjen-Moore (TM) test or Peirce’s criterion. This is followed by computation of a test statistic (TS) which is a random variable. In this paper, we assume TS to have either a Gamma or a Gaussian distribution and calculate the threshold accordingly. We provide closed-form expressions of probability of false alarm and probability of miss-detection under both assumptions. We show that Gaussian assumption of TS is more suited in presence of an SSDF attack when compared with the Gamma assumption. We also compare the detection performance for various values of p and show that p=3 along with the Gaussian assumption is the best amongst all the cases considered.

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“…The effect of cooperative spectrum sensing which considered as a critical issue of cognitive radio technology which it needs to detect the presence of primary users accurately and swiftly using many techniques for make a decision was discussed in [11]. New cooperative spectrum sensing process schemes were discussed in [12] [13]. Another technique about the cooperative spectrum sensing process denoted by q-out-of-n rule, where n denotes the number of users that cooperate in the network (the base station decides the presence of primaries if number of users (q) or more report "1") was illustrated in [14].…”

Section: Introductionmentioning

confidence: 99%

Performance of Analysis Cognitive Radio with Cooperative Sensing under Malicious Attacks over Nakagami Faded Channels

Shazly¹,

Saafan²,

Badawy³

et al. 2016

WET

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The different realistic propagation channels are faced frequently the multipath fading environments. The main goal of this system design (cognitive radio network) is to improve the efficiency of spectrum access on a non-interfering basis. This system achieves high utilization for the limited spectrum in order to fulfill needs for all users' demands which are considered as a problem in wireless communications due to rapidly increasing in wireless applications and service. This system is exposed to attack due to the vulnerabilities existence in this system. So, the main outcome of this paper is to investigate the performance of the cooperative sensing in cognitive radio networks under malicious attacks over different channel impairments, and to illustrate the most suitable individual probability of detection ( ) dNAK P in real faded channel by using Nakagami model. This paper illustrates the effectiveness of the attacks and fading on the performance of spectrum sensing process.

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Malicious user suppression for cooperative spectrum sensing in cognitive radio networks using Dixon's outlier detection method

Cited by 29 publications

References 10 publications

Block Outlier Methods for Malicious User Detection in Cooperative Spectrum Sensing

Block Outlier Methods for Malicious User Detection in Cooperative Spectrum Sensing

Performance of p-Norm Detector in Cognitive Radio Networks with Cooperative Spectrum Sensing in Presence of Malicious Users

Performance of Analysis Cognitive Radio with Cooperative Sensing under Malicious Attacks over Nakagami Faded Channels

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