{"title":"A single iteration belief propagation algorithm to minimize the effects of primary user emulation attacks","authors":"Sasa Maric, S. Reisenfeld, L. Goratti","doi":"10.1109/ISPACS.2016.7824676","DOIUrl":null,"url":null,"abstract":"This paper presents a method to alleviate the effects of primary user emulation attacks in cognitive radio (CR) networks. The proposed method uses a simplified belief propagation (BP) algorithm that is able to identify whether a transmitter is a legitimate primary user or an attacker. In a primary user emulation attack (PUEA) a transmitter impersonates a primary user (PU) in order to deceive secondary users (SU) into believing that a channel is occupied. As a result, secondary users must vacate the channel immediately. This paper presents a simplified belief propagation method as a defence strategy against primary user emulation attacks. In our method each secondary user examines an incoming signal from a transmitter and determines with a certain probability whether the transmitter is a legitimate user or not. This probability is known as the belief. The beliefs at each secondary user are reconciled and a final belief is compared to a predefined threshold. If the final belief is below the threshold, the user is identified as an attacker. If it is above the threshold, the user is deemed a primary user. This result is then propagated throughout the network so that all users on the network are aware of the attacker. In this paper, we present a method based on the belief propagation framework. The proposed method converges in a single iteration; this is the result of a redefined messaging protocol and a simplified belief equation. As a result, the computational complexity of the method is reduced significantly, while still maintaining a high level of accuracy.","PeriodicalId":131543,"journal":{"name":"2016 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS)","volume":"258 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISPACS.2016.7824676","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 10
Abstract
This paper presents a method to alleviate the effects of primary user emulation attacks in cognitive radio (CR) networks. The proposed method uses a simplified belief propagation (BP) algorithm that is able to identify whether a transmitter is a legitimate primary user or an attacker. In a primary user emulation attack (PUEA) a transmitter impersonates a primary user (PU) in order to deceive secondary users (SU) into believing that a channel is occupied. As a result, secondary users must vacate the channel immediately. This paper presents a simplified belief propagation method as a defence strategy against primary user emulation attacks. In our method each secondary user examines an incoming signal from a transmitter and determines with a certain probability whether the transmitter is a legitimate user or not. This probability is known as the belief. The beliefs at each secondary user are reconciled and a final belief is compared to a predefined threshold. If the final belief is below the threshold, the user is identified as an attacker. If it is above the threshold, the user is deemed a primary user. This result is then propagated throughout the network so that all users on the network are aware of the attacker. In this paper, we present a method based on the belief propagation framework. The proposed method converges in a single iteration; this is the result of a redefined messaging protocol and a simplified belief equation. As a result, the computational complexity of the method is reduced significantly, while still maintaining a high level of accuracy.