Countermeasures for Primary User Emulation Attack: A Comprehensive Review

Mishra, Nikita; Srivastava, Sumit; Sharan, Shivendra Nath

doi:10.1007/s11277-020-07600-y

Cited by 5 publications

(2 citation statements)

References 92 publications

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“…Mishra et al implemented deep learning-based denoting by improving the deep network structure. In the literature [8], the authors used ResNet to remove rain noise from photographs and the experimental results showed that the model has superior denoting performance [9]. Gupta [8].…”

Section: Related Workmentioning

confidence: 99%

Automatic Recognition of Communication Signal Modulation Based on the Multiple‐Parallel Complex Convolutional Neural Network

Huang

et al. 2021

Wireless Communications and Mobile Computing

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This paper implements a deep learning-based modulation pattern recognition algorithm for communication signals using a convolutional neural network architecture as a modulation recognizer. In this paper, a multiple-parallel complex convolutional neural network architecture is proposed to meet the demand of complex baseband processing of all-digital communication signals. The architecture learns the structured features of the real and imaginary parts of the baseband signal through parallel branches and fuses them at the output according to certain rules to obtain the final output, which realizes the fitting process to the complex numerical mapping. By comparing and analyzing several commonly used time-frequency analysis methods, a time-frequency analysis method that can well highlight the differences between different signal modulation patterns is selected to convert the time-frequency map into a digital image that can be processed by a deep network. In order to fully extract the spatial and temporal characteristics of the signal, the CLP algorithm of the CNN network and LSTM network in parallel is proposed. The CNN network and LSTM network are used to extract the spatial features and temporal features of the signal, respectively, and the fusion of the two features as well as the classification is performed. Finally, the optimal model and parameters are obtained through the design of the modulation recognizer based on the convolutional neural network and the performance analysis of the convolutional neural network model. The simulation experimental results show that the improved convolutional neural network can produce certain performance gains in radio signal modulation style recognition. This promotes the application of machine learning algorithms in the field of radio signal modulation pattern recognition.

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Section: Related Workmentioning

confidence: 99%

Automatic Recognition of Communication Signal Modulation Based on the Multiple‐Parallel Complex Convolutional Neural Network

Huang

et al. 2021

Wireless Communications and Mobile Computing

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“…Nevertheless, cloud radio access network (CRAN) faces serious challenges of security threats and trust problems because of its transmission nature and selforganizing feature. Therefore, due to limited spectral and energy resources and increased number of connected devices, the malicious users cause spectrum unavailability to legitimate users by competing for the available spectrum with the PUs [4]. Therefore, there is a need for security approaches to sense and remove attacks from the system without affecting spectral and energy utilization and causing delay.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Spectral and Energy Efficiencies in a Cognitive Radio CRAN with PUEAs

Odwa

Chen

2023

Wireless Communications and Mobile Computing

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In a cloud radio access network, malicious users mitigate the attributes of primary users in order to occupy a specific idle spectrum band by sending false signals or carry out a denial of service attack. Moreover, with the increase in number of users and limited spectral and energy resources, the malicious users will compete for the spectrum with legitimate users, thus resulting in increase in spectrum scarcity problem. The most widely used defense approach against malicious users is the received signal strength method. However, harmful users can still imitate signal attributes and transmit powers of the primary users. Therefore, in order to elaborate the best method to tackle this vulnerability and hence make more spectrum available, the modified adaptive orthogonal matching pursuit localization algorithm is proposed to detect harmful users existing in the network. However, in order to elaborate the convergence speed of the proposed method, the regularized particle filter algorithm is applied to evaluate the performance of the modified adaptive orthogonal matching pursuit under real-time conditions. The restricted isometry property is used for the performance evaluation. Further, spectral and energy efficiencies are used in the simulation results for performance evaluation, in order to observe spectrum and energy utilization efficiencies. The simulation results show that the proposed method is better in terms of computational complexity, spectral efficiency, and energy efficiency compared to other matching pursuit approaches.

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