When SDN Meets Low-rate Threats: A Survey of Attacks and Countermeasures in Programmable Networks

IF 23.8 1区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS
Dan Tang, Rui Dai, Yudong Yan, Keqin Li, Wei Liang, Zheng Qin
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引用次数: 0

Abstract

Low-rate threats are a class of attack vectors that are disruptive and stealthy, typically crafted for security vulnerabilities. They have been the significant concern for cyber security, impacting both conventional IP-based networks and emerging Software-Defined Networking (SDN). SDN is a revolutionary architecture that separates the control and data planes, offering advantages such as enhanced manageability, flexibility, and network programmability, as well as the ability to introduce new solutions to address security threats. However, its innovative design also poses new vulnerabilities and threats, especially susceptibility to low-rate threats. To this end, this paper presents a comprehensive overview of low-rate threats in programmable networks. It explores low-rate threats and countermeasures within the SDN architecture, encompassing the data plane, control plane, control channel, and application plane, together with traditional low-rate threats and countermeasures in SDN. Furthermore, the paper offers detailed insight into threats and countermeasures against low-rate attacks exploiting SDN vulnerabilities and low-rate attacks related to Programmable Data Plane (PDP). Additionally, it presents a comparative analysis and discussion of low-rate attacks versus high-volume attacks, along with suggestions for enhancing SDN security. This thorough review aims to assist researchers in developing more resilient and dependable countermeasures against low-rate threats in programmable networks.
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来源期刊
ACM Computing Surveys
ACM Computing Surveys 工程技术-计算机:理论方法
CiteScore
33.20
自引率
0.60%
发文量
372
审稿时长
12 months
期刊介绍: ACM Computing Surveys is an academic journal that focuses on publishing surveys and tutorials on various areas of computing research and practice. The journal aims to provide comprehensive and easily understandable articles that guide readers through the literature and help them understand topics outside their specialties. In terms of impact, CSUR has a high reputation with a 2022 Impact Factor of 16.6. It is ranked 3rd out of 111 journals in the field of Computer Science Theory & Methods. ACM Computing Surveys is indexed and abstracted in various services, including AI2 Semantic Scholar, Baidu, Clarivate/ISI: JCR, CNKI, DeepDyve, DTU, EBSCO: EDS/HOST, and IET Inspec, among others.
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