Spectrum Sharing Security and Attacks in CRNs: a Review

Alhakami, Wajdi; Mansour, Ali; Safdar, Ghazanfar Ali

doi:10.14569/ijacsa.2014.050111

Cited by 17 publications

(17 citation statements)

References 97 publications

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“…It utilizes frequency‐hopping spread spectrum technique. It is the spread spectrum scheme in which the data change their frequency according to the PN sequence pattern generated [25].…”

Section: Methodsmentioning

confidence: 99%

Authentication scheme to combat a primary user emulation attack against cognitive radio users

Jayapalan

Thenmozhi

2015

Security Comm Networks

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Cognitive radios are a good fit for overcoming inefficient usage of the available radio spectrum, allowing secondary users to utilize the spectrum when the primary licensed users are not consuming it. This requires secondary users to continuously observe their spectrum utilization to prevent interfering with the primary user's transmissions. However, an adversary can exploit this to gain unfair use of the spectrum. Such an attack is known a primary user emulation attack. To avoid these attacks, a method of authenticating the primary user's signal is required. This work addresses embedding such an authentication message with the binary phase shift keying and eight‐phase shift keying modulation techniques, parity bits of the error control codes, and pre‐coder bit of the modified duo‐binary scheme, and analyses of the error performance have been carried out with the aid of their performance graphs. Copyright © 2015 John Wiley & Sons, Ltd.

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“…It utilizes frequency‐hopping spread spectrum technique. It is the spread spectrum scheme in which the data change their frequency according to the PN sequence pattern generated [25].…”

Section: Methodsmentioning

confidence: 99%

Authentication scheme to combat a primary user emulation attack against cognitive radio users

Jayapalan

Thenmozhi

2015

Security Comm Networks

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“…3 are the output of each random trees. These trees are combined to form the aggregated estimation to train the forest using: 9 d ( , 9 ) = g [ 9 ( , , 9 )] (7) where is the expectation with respect to the random parameter, conditionally, on and the data set 9 . Once the forest is trained, each tree can predict independently to output values using the following equation:…”

Section: ) Random Forestmentioning

confidence: 99%

A Novel Jamming Attacks Detection Approach Based on Machine Learning for Wireless Communication

Arjoune

Salahdine

Islam

et al. 2020

2020 International Conference on Information Networking (ICOIN)

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Jamming attacks target a wireless network creating an unwanted denial of service. 5G is vulnerable to these attacks despite its resilience prompted by the use of millimeter wave bands. Over the last decade, several types of jamming detection techniques have been proposed, including fuzzy logic, game theory, channel surfing, and time series. Most of these techniques are inefficient in detecting smart jammers. Thus, there is a great need for efficient and fast jamming detection techniques with high accuracy. In this paper, we compare the efficiency of several machine learning models in detecting jamming signals. We investigated the types of signal features that identify jamming signals, and generated a large dataset using these parameters. Using this dataset, the machine learning algorithms were trained, evaluated, and tested. These algorithms are random forest, support vector machine, and neural network. The performance of these algorithms was evaluated and compared using the probability of detection, probability of false alarm, probability of miss detection, and accuracy. The simulation results show that jamming detection based random forest algorithm can detect jammers with a high accuracy, high detection probability and low probability of false alarm.

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“…Therefore, the channel which has the highest stability is determined to be the control channel for exchanging control frames between a pair of CUs. Another approach of MAC protocols uses CCC to facilitate the spectrum sharing process for exchanging the handshaking frames between two CUs is introduced in [8]. This leads to perform the initial synchronisation of the licensed channel over the CCC.…”

Section: Related Workmentioning

confidence: 99%

Performance analysis of a novel decentralised MAC protocol for cognitive radio networks

Alhakami

Mansour

Safdar

2016

2016 IEEE Asia Pacific Conference on Wireless and Mobile (APWiMob)

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Abstract-Due to the demand of emerging Cognitive Radio (CR) technology to permits using the unused licensed spectrum parts by cognitive users (CUs) to provide opportunistic and efficient utilisation of the white spaces. This requires deploying a CR MAC with the required characteristics to coordinate the spectrum access among CUs. Therefore, this paper presents the design and implementation of a novel Medium Access Control (MAC) protocol for decentralised CRNs (MCRN). The protocol provides efficient utilisations of the unused licensed channels and enables CUs to exchange data successfully over licensed channels. This is based on the observation procedure of sensing the status of the Licensed Users (LUs) are ON or OFF over the licensed channels. The protocol is validated with the comparison procedure against two different benchmark protocols in terms of the network performance; communication time and throughput. Therefore, performance analysis demonstrated that the proposed MCRN perform better and achieve higher throughput and time benefits than the benchmarks protocols.

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Spectrum Sharing Security and Attacks in CRNs: a Review

Cited by 17 publications

References 97 publications

Authentication scheme to combat a primary user emulation attack against cognitive radio users

Authentication scheme to combat a primary user emulation attack against cognitive radio users

A Novel Jamming Attacks Detection Approach Based on Machine Learning for Wireless Communication

Performance analysis of a novel decentralised MAC protocol for cognitive radio networks

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