Topology Poisoning Attack in SDN-Enabled Vehicular Edge Network

被引:32
作者
Wang, Jiadai [1 ]
Tan, Yawen [1 ]
Liu, Jiajia [2 ]
Zhang, Yanning [2 ]
机构
[1] Xidian Univ, Sch Cyber Engn, State Key Lab Integrated Serv Networks, Xian 710071, Peoples R China
[2] Northwestern Polytech Univ, Sch Cybersecur, Natl Engn Lab Integrated Aerosp Ground Ocean Big, Xian 710072, Peoples R China
基金
中国国家自然科学基金;
关键词
Network topology; Topology; Security; Computer architecture; Relays; Servers; Edge computing; network security; software defined networking; vehicular network; MANAGEMENT; ALLOCATION; EFFICIENT;
D O I
10.1109/JIOT.2020.2984088
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The development of the Internet of Vehicles (IoV) has made people's lives and travels safer, more efficient, and more comfortable. The combination of edge computing and IoV can provide processing and storage capabilities close to vehicles, thus becoming a potential paradigm. At this time, the software-defined networking (SDN) architecture is extremely necessary to realize centralized control and convenient management for complex and dynamic vehicular edge networks. However, as the brain of the SDN architecture, little attention has been paid to the security of the SDN controller. Once the controller is threatened, severe global chaos may happen. Therefore, in this article, we study the attack against the SDN controller, which is the topology poisoning attack. We successfully implement this attack in four mainstream controllers and analyze its impact from multiple levels. To the best of our knowledge, we are the first to study this attack in the vehicular edge network. In addition, in view of the counter-attacks of the existing defence mechanisms, we propose an attack-tolerance scheme based on deep reinforcement learning (DRL) to enhance the vehicular edge network with a certain degree of self-recovery.
引用
收藏
页码:9563 / 9574
页数:12
相关论文
共 31 条
[1]  
[Anonymous], 2013, PLAYING ATARI DEEP R
[2]   Efficient Multi-User Computation Offloading for Mobile-Edge Cloud Computing [J].
Chen, Xu ;
Jiao, Lei ;
Li, Wenzhong ;
Fu, Xiaoming .
IEEE-ACM TRANSACTIONS ON NETWORKING, 2016, 24 (05) :2827-2840
[3]   From Theory to Experimental Evaluation: Resource Management in Software-Defined Vehicular Networks [J].
Fontes, Ramon Dos Reis ;
Campolo, Claudia ;
Rothenberg, Christian Esteve ;
Molinaro, Antonella .
IEEE ACCESS, 2017, 5 :3069-3076
[4]   Dense-Device-Enabled Cooperative Networks for Efficient and Secure Transmission [J].
Han, Shuai ;
Xu, Sai ;
Meng, Weixiao ;
Li, Cheng .
IEEE NETWORK, 2018, 32 (02) :100-106
[5]   Poisoning Network Visibility in Software-Defined Networks: New Attacks and Countermeasures [J].
Hong, Sungmin ;
Xu, Lei ;
Wang, Haopei ;
Gu, Guofei .
22ND ANNUAL NETWORK AND DISTRIBUTED SYSTEM SECURITY SYMPOSIUM (NDSS 2015), 2015,
[6]  
Hussein A, 2017, 2017 FOURTH INTERNATIONAL CONFERENCE ON SOFTWARE DEFINED SYSTEMS (SDS), P67, DOI 10.1109/SDS.2017.7939143
[7]  
Kalinin M, 2016, I C INF COMM TECH CO, P533, DOI 10.1109/ICTC.2016.7763528
[8]   Edge AI: On-Demand Accelerating Deep Neural Network Inference via Edge Computing [J].
Li, En ;
Zeng, Liekang ;
Zhou, Zhi ;
Chen, Xu .
IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, 2020, 19 (01) :447-457
[9]  
Li MJ, 2018, 2018 EUROPEAN CONFERENCE ON OPTICAL COMMUNICATION (ECOC)
[10]  
Li ZS, 2018, TRANS DISTRIB CONF