TY - JOUR
T1 - Topology Poisoning Attack in SDN-Enabled Vehicular Edge Network
AU - Wang, Jiadai
AU - Tan, Yawen
AU - Liu, Jiajia
AU - Zhang, Yanning
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2020/10
Y1 - 2020/10
N2 - 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.
AB - 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.
KW - Edge computing
KW - network security
KW - software defined networking
KW - vehicular network
UR - http://www.scopus.com/inward/record.url?scp=85092703397&partnerID=8YFLogxK
U2 - 10.1109/JIOT.2020.2984088
DO - 10.1109/JIOT.2020.2984088
M3 - 文章
AN - SCOPUS:85092703397
SN - 2327-4662
VL - 7
SP - 9563
EP - 9574
JO - IEEE Internet of Things Journal
JF - IEEE Internet of Things Journal
IS - 10
M1 - 9050651
ER -