Avoiding monopolization: mutual-aid collusive attack detection in cooperative spectrum sensing

2016 ◽  
Vol 60 (5) ◽  
Author(s):  
Jingyu Feng ◽  
Guangyue Lu ◽  
Yuqing Zhang ◽  
Honggang Wang
Keyword(s):  
Spectrum Sensing ◽  
Cooperative Spectrum Sensing ◽  
Attack Detection ◽  
Mutual Aid ◽  
Collusive Attack

scholarly journals Research on SSDF Attack Detection Algorithm in Cognitive Internet of Things

2021 ◽  
Author(s):  
Miao Liu ◽  
Di Yu ◽  
Zhuo-Miao Huo ◽  
Zhen-Xing Sun
Keyword(s):  
Internet Of Things ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Spectrum Sensing ◽  
Cooperative Spectrum Sensing ◽  
Hidden Markov ◽  
Detection Algorithm ◽  
Attack Detection ◽  
Iot Devices ◽  
Cognitive Internet Of Things

Abstract The Internet of Things (IoT) is a new paradigm for connecting various heterogeneous networks.cognitive radio (CR) adopts cooperative spectrum sensing (CSS) to realize the secondary utilization of idle spectrum by unauthorized IoT devices,so that IoT objects can effectively use spectrum resources.However, the abnormal IoT devices in the cognitive Internet of Things will disrupt the CSS process. For this attack, we propose a spectrum sensing strategy based on the weighted combining of the Hidden Markov Model. In this method, Hidden Markov Model is used to detect the probability of malicious attack of each node and report it to the fusion center (FC). FC allocates a reasonable weight value according to the evaluation of the submitted observation results to improve the accuracy of the sensing results.Simulation results show that the detection performance of spectrum sensing data forgery(SSDF) attack in cognitive Internet of Things is better than that of K rank criterion in hard combining.

scholarly journals An SETM Algorithm for Combating SSDF Attack in Cognitive Radio Networks

2020 ◽  
Vol 2020 ◽  
pp. 1-9 ◽  
Author(s):  
S. Tephillah ◽  
J. Martin Leo Manickam
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Trust Management ◽  
Cooperative Spectrum Sensing ◽  
Radio Networks ◽  
Second Phase ◽  
Common Channel ◽  
Secondary Users ◽  
Management Algorithm ◽  
Different Types

Security is a pending challenge in cooperative spectrum sensing (CSS) as it employs a common channel and a controller. Spectrum sensing data falsification (SSDF) attacks are challenging as different types of attackers use them. To address this issue, the sifting and evaluation trust management algorithm (SETM) is proposed. The necessity of computing the trust for all the secondary users (SUs) is eliminated based on the use of the first phase of the algorithm. The second phase is executed to differentiate the random attacker and the genuine SUs. This reduces the computation and overhead costs. Simulations and complexity analyses have been performed to prove the efficiency and appropriateness of the proposed algorithm for combating SSDF attacks.

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