{"title":"车载自组网中多重干扰攻击者数量估计","authors":"Liang Pang, Pengze Guo, Xiao Chen, Jiabin Li, Zhi Xue","doi":"10.1109/ICCSNT.2017.8343720","DOIUrl":null,"url":null,"abstract":"In Vehicular Ad Hoc Network (VANET), due to the openness of wireless medium and the city-wide applied region, wireless communications are vulnerable to malicious multiple jamming attackers. Such jamming attack can seriously disable the preset functions of VANET. Jammer localization method is proposed to eliminate the attacker recently. However, it becomes invalid when there are multiple attackers. To address this issue, we propose a novel method to determine the attacker number and classify the corresponding data set. Our proposed method first uses the moving features of vehicles and spatial features of jammers as basis to divide the data set into several point sets. Then, the method uses the distribution of no-jammed points to group the point sets. And the result is accordingly obtained through this process. The simulation results show that our proposed method is effective and has many advantages compared to the traditional method.","PeriodicalId":163433,"journal":{"name":"2017 6th International Conference on Computer Science and Network Technology (ICCSNT)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"Estimating the number of multiple jamming attackers in Vehicular Ad Hoc Network\",\"authors\":\"Liang Pang, Pengze Guo, Xiao Chen, Jiabin Li, Zhi Xue\",\"doi\":\"10.1109/ICCSNT.2017.8343720\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In Vehicular Ad Hoc Network (VANET), due to the openness of wireless medium and the city-wide applied region, wireless communications are vulnerable to malicious multiple jamming attackers. Such jamming attack can seriously disable the preset functions of VANET. Jammer localization method is proposed to eliminate the attacker recently. However, it becomes invalid when there are multiple attackers. To address this issue, we propose a novel method to determine the attacker number and classify the corresponding data set. Our proposed method first uses the moving features of vehicles and spatial features of jammers as basis to divide the data set into several point sets. Then, the method uses the distribution of no-jammed points to group the point sets. And the result is accordingly obtained through this process. The simulation results show that our proposed method is effective and has many advantages compared to the traditional method.\",\"PeriodicalId\":163433,\"journal\":{\"name\":\"2017 6th International Conference on Computer Science and Network Technology (ICCSNT)\",\"volume\":\"18 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 6th International Conference on Computer Science and Network Technology (ICCSNT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCSNT.2017.8343720\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 6th International Conference on Computer Science and Network Technology (ICCSNT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCSNT.2017.8343720","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Estimating the number of multiple jamming attackers in Vehicular Ad Hoc Network
In Vehicular Ad Hoc Network (VANET), due to the openness of wireless medium and the city-wide applied region, wireless communications are vulnerable to malicious multiple jamming attackers. Such jamming attack can seriously disable the preset functions of VANET. Jammer localization method is proposed to eliminate the attacker recently. However, it becomes invalid when there are multiple attackers. To address this issue, we propose a novel method to determine the attacker number and classify the corresponding data set. Our proposed method first uses the moving features of vehicles and spatial features of jammers as basis to divide the data set into several point sets. Then, the method uses the distribution of no-jammed points to group the point sets. And the result is accordingly obtained through this process. The simulation results show that our proposed method is effective and has many advantages compared to the traditional method.