[1]李 源,谢一臻,王永建,等.面向车联网泛洪攻击的流量异常检测方法[J].南京理工大学学报(自然科学版),2020,44(04):454-461.[doi:10.14177/j.cnki.32-1397n.2020.44.04.010]
 Li Yuan,Xie Yizhen,Wang Yongjian,et al.Traffic anomaly detection method for vehicular ad-hoc networkflooding attack[J].Journal of Nanjing University of Science and Technology,2020,44(04):454-461.[doi:10.14177/j.cnki.32-1397n.2020.44.04.010]
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面向车联网泛洪攻击的流量异常检测方法()
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《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

卷:
44卷
期数:
2020年04期
页码:
454-461
栏目:
出版日期:
2020-08-30

文章信息/Info

Title:
Traffic anomaly detection method for vehicular ad-hoc networkflooding attack
文章编号:
1005-9830(2020)04-0454-08
作者:
李 源1谢一臻2王永建3江 虹4
1.西南科技大学 信息工程学院,四川 绵阳 620010; 2.北京邮电大学 国际学院,北京 100876; 3.国家计算机网络与信息安全管理中心,北京 100031; 4.西南科技大学 信息工程学院,四川 绵阳 620010
Author(s):
Li Yuan1Xie Yizhen2Wang Yongjian3Jiang Hong4
1.School of Information Engineering,Southwest University of Science and Technology,Mianyang 620010,China; 2.International College,Beijing University of Posts and Telecommunications,Beijing 100876,China; 3.National Computer Network and Information Securi
关键词:
车联网 泛洪攻击 流量异常检测 Hurst自相似度
Keywords:
vehicular ad-hoc network flood attack traffic detection Hurst self-similarity
分类号:
TP393.1
DOI:
10.14177/j.cnki.32-1397n.2020.44.04.010
摘要:
为了有效检测车联网环境下的泛洪攻击,缓解泛洪攻击对车联网的不良影响,该文提出了面向车联网环境下针对泛洪攻击的轻量化流量异常检测方法。通过在路侧单元使用Hurst自相似参数估计方法计算车联网数据包流量的自相似变化曲线,检测流量异常变化,并上传异常时段的数据包信息到云端,云端统计各节点发送数据包情况,通过检测数据包大小异常变化来检测发起泛洪攻击的恶意节点。通过对车联网实际网络流量仿真计算结果表明,该流量异常检测方法能在低中高3种泛洪攻击强度下,有效检测出恶意节点。
Abstract:
In order to effectively detect flooding attacks and mitigate the adverse effects of flooding attacks in VANET,this paper proposes a lightweight traffic anomaly detection method for flooding attacks in vehicular ad-hoc network(VANET). By using the Hurst self-similar parameter estimation method in the roadside unit(RSU)to calculate the self-similar change curve of the data traffic,this method can detect abnormal traffic and upload the packet information during the abnormal period to the Cloud.The cloud counts the packet sent by each node,and identifies the malicious node that initiates the flooding attacks by detecting the abnormal total data. The simulation results of the actual network traffic of VANET show that the traffic anomaly detection method can effectively detect malicious nodes under three types of flooding attacks intensity:low,medium and high.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2020-03-28 修回日期:2020-07-02
作者简介:李源(1994-),男,硕士,主要研究方向:车联网安全,E-mail:Liyuan_3033@163.com。
引文格式:李源,谢一臻,王永建,等. 面向车联网泛洪攻击的流量异常检测方法[J]. 南京理工大学学报,2020,44(4):454-461.
投稿网址:http://zrxuebao.njust.edu.cn
更新日期/Last Update: 2020-08-30