A combination of double sided neighbor distance and Genetic Algorithm in cooperative spectrum sensing against malicious users

Gul, Noor; Naveed, A.; Elahi, Atif; Tahir, SaleemKhattak; Qureshi, Ijaz Mansoor

doi:10.1109/ibcast.2017.7868137

Cited by 20 publications

(15 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this paper, the proposed method outcomes are verified and tested against the existence of an opposite malicious user (OMU), random opposite malicious user (ROMU), always yes malicious user (AYMU), and always no malicious user (ANMU) [32,39]. An AYMU provides a high-energy signal to the FC irrespective of the actual PU spectrum status, thus increasing false alarm probability and reducing throughput for the SUs.…”

Section: Introductionmentioning

confidence: 99%

“…The MV-HDF scheme then decides globally about the actual status of the PU. In comparison with [39], this work optimizes the detection, false alarm, and error results when MUs take low and high SNR of the channel in comparison with NSUs. The proposed scheme is tested at different levels of SNR and increasing number of cooperative users with simple soft decision fusion (SDF) and hard decision fusion (HDF) schemes in [28][29][30][31][32][33][34][35][36].…”

Section: Introductionmentioning

confidence: 99%

“…The study in [39] is based on the combination of double-sided neighbor distance (DSND) algorithm-based GA, where MUs are first identified using DSND and then GA is used for the selection of the best spectrum sensing results at the end of the given number of iterations. The best selected results of the GA in [39] are followed by the majority voting hard decision (MV-HDF) to take a global decision of the PU spectrum. Unlike [39], this proposed method required no additional steps for the identification of MUs using the DSND algorithm.…”

Section: Introductionmentioning

confidence: 99%

“…The best selected results of the GA in [39] are followed by the majority voting hard decision (MV-HDF) to take a global decision of the PU spectrum. Unlike [39], this proposed method required no additional steps for the identification of MUs using the DSND algorithm. Cooperative SUs including both normal SUs (NSUs) and MUs report to the FC by their single-bit hard binary decisions.…”

Section: Introductionmentioning

confidence: 99%

“…GA can be utilized to find optimized solutions to examine problems through the application of biologically inspired methods [38][39][40]. Holland referred to the chromosomes as strings of binary symbols encoding a candidate solution to the given problem.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Defense against Malicious Users in Cooperative Spectrum Sensing Using Genetic Algorithm

Gul

Qureshi

Elahi

et al. 2018

International Journal of Antennas and Propagation

Self Cite

View full text Add to dashboard Cite

In cognitive radio network (CRN), secondary users (SUs) try to sense and utilize the vacant spectrum of the legitimate primary user (PU) in an efficient manner. The process of cooperation among SUs makes the sensing more authentic with minimum disturbance to the PU in achieving maximum utilization of the vacant spectrum. One problem in cooperative spectrum sensing (CSS) is the occurrence of malicious users (MUs) sending false data to the fusion center (FC). In this paper, the FC takes a global decision based on the hard binary decisions received from all SUs. Genetic algorithm (GA) using one-to-many neighbor distance along with z-score as a fitness function is used for the identification of accurate sensing information in the presence of MUs. The proposed scheme is able to avoid the effect of MUs in CSS without identification of MUs. Four types of abnormal SUs, opposite malicious user (OMU), random opposite malicious user (ROMU), always yes malicious user (AYMU), and always no malicious user (ANMU), are discussed in this paper. Simulation results show that the proposed hard fusion scheme has surpassed the existing hard fusion scheme, equal gain combination (EGC), and maximum gain combination (MGC) schemes by employing GA.

show abstract

Section: Introductionmentioning

confidence: 99%