PROLEMus: A Proactive Learning-Based MAC Protocol Against PUEA and SSDF Attacks in Energy Constrained Cognitive Radio Networks

Patnaik, Milan; Kamakoti, V.; Matyáš, Václav; Řehák, Vojtěch

doi:10.1109/tccn.2019.2913397

Cited by 35 publications

(31 citation statements)

References 20 publications

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“…This is because of identifying two hypotheses, first, by applying the STALOG function, and then measuring the false alarm and detection by means of Logical OR rule. With this, the SD is said to be reduced using OS‐LNCC method by 20% compared to [1], 52% compared to [2], 55% compared to [5] and 59% compared to [16].…”

Section: Methodsmentioning

confidence: 99%

Optimised Levenshtein centroid cross‐layer defence for multi‐hop cognitive radio networks

Ganesh

Kumar

Kumar³

2020

IET Communications

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Cognitive radio networks (CRN) make use of dynamic spectrum access to communicate opportunistically. Unlicensed users severely affect the spectrum sensing outcomes in CRN. Primary user emulation attack (PUEA) and spectrum sensing data falsification (SSDF) have become a paramount concern in CRNs. It is especially challenging when both masquerading (in the physical layer) and falsification (in the data link layer) occur by providing false spectrum reports. Existing methods to detect such attacks cannot be utilised in scenarios with multi-hop CRN. In this study, to mitigate attack against PUEA and SSDF, a method called optimised sensing and Levenshtein nearest centroid classification (OS-LNCC) for multi-hop CRN is presented. First, a network model for multi-hop CRN is designed. Next, a probable density optimal logical sensing model is designed to alleviate the problems related to falsification of spectrum reports. Here, the falsification of spectrum reports is overcome by exploiting dual factors, that is, probability for false alarm and probability for detection according to the departure rate of primary user (PU). With these dual factors, optimal logical sensing is made, therefore improving the throughput with minimum delay. Finally, each cognitive radio (CR) user evaluates its current sensing information to existing sensing classes through the Levenshtein distance function. Based on quantitative variables, the prediction function of each sensing class is measured using nearest centroid (NC) classifier and the sensing report is classified into either presence or absence of PU. These predictive classes are then integrated at the fusion centre so that robust mitigation against PUEA and SSDF is made. Computer simulation outputs show that OS-LNCC method performs better than the conventional methods using metrics such as sensing delay by 47%, percentage of error in prediction by 46% and throughput by 45%.

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Section: Methodsmentioning

confidence: 99%

Optimised Levenshtein centroid cross‐layer defence for multi‐hop cognitive radio networks

Ganesh

Kumar

Kumar³

2020

IET Communications

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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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“…In previous works, 6,15 the authors propose a distributed trust‐based model for an efficient channel's leasing policy in which a Primary User (PU) willingly leases a portion of its channel access to unlicensed Secondary Users (SUs), and in return, these provide cooperative services by adding intentional jamming to counter eavesdropping and employ cooperative relaying to enhance the quality of communication. Nevertheless, the authors in previous studies 10,12,16,19,20 model the cooperative communication in a spectrum aware environment as a statistical framework and hence propose to employ the cooperative adversarial model for analyzing SSDF's consequences on DSA‐enabled CR‐IIoTs.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In state‐of‐the‐art, 20,21 the researchers propose certain exclusive trust cum cryptographic frameworks to counter SSDF in a Cognitive Radio‐environment. The Theory of Uncertain Reasoning, and Privacy Preserving Public Key techniques are recently explored.…”

Section: Literature Reviewmentioning

confidence: 99%

Conjoint belief adaption functionality for statistically secure communication paradigm for CR‐IIoTs

Indra

Dhurandher

Raj

2020

Int J Communication

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SummaryIn an infrastructure‐critical manufacturing industry, continuous monitoring of the assembly lines with internet‐integrated sensors in an unforeseeable, stochastic, and harsh environment becomes indispensable for real‐time fault detection, location, and isolation. State‐of‐the‐art literature suggest that the Cognitive Radio‐based Industrial Internet of Things (CR‐IIoTs), a spectrum aware wireless communication paradigm, is best suited for monitoring as they are proficient in overcoming spectrum scarcity in a 2.4‐GHz industrial, scientific, and medical (ISM) band and guarantee a threshold Quality of Service (QoS) in hostile environs. Nonetheless, CR‐IIoTs and the Cognitive Radio‐based network archetype are susceptible to internal and external security threats at the application layer and vulnerable to Spectrum Sensing Data Falsification (SSDF) at Data Link Layer (DLL). In CR‐IIoTs, cooperative spectrum sensing (CSS) is a competent technique to ameliorate the performance of Primary User (PU) detection and optimizes the idle licensed spectrum bands access management. In this paper, a novel belief adaption framework in conjunction with an optimized secure communication scheme (termed as CBAF) based on the Certificateless‐Public Key Cryptography (CL‐PKC) is proposed for robust CSS in CR‐IIoTs. CBAF proficiently provides full‐Perfect Forward Secrecy, Ephemeral Secret Leakage‐Resilience, Public Key Replacement‐Resilience Malicious but Passive KGC‐Resilience, and Key Escrow Resilience. Simulation analysis pertaining to network performance indices ascertains the efficacy of CBAF, as it outperforms the state‐of‐the‐art in throughput, packet delivery ratio, and communication overhead by 8.5%, 6.7%, and 19.9% respectively. Under internal attacks, CBAF outperforms the best from state‐of‐the‐art by 11.3% in On‐Off attack, 19.7% in Data Tampering attack, 21.1% in SDF attack, and 13.4% in DDoS attack.

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PROLEMus: A Proactive Learning-Based MAC Protocol Against PUEA and SSDF Attacks in Energy Constrained Cognitive Radio Networks

Cited by 35 publications

References 20 publications

Optimised Levenshtein centroid cross‐layer defence for multi‐hop cognitive radio networks

Optimised Levenshtein centroid cross‐layer defence for multi‐hop cognitive radio networks

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

Conjoint belief adaption functionality for statistically secure communication paradigm for CR‐IIoTs

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