DoSGuard: Mitigating Denial-of-Service Attacks in Software-Defined Networks

Author:

Li JishuaiORCID,Tu Tengfei,Li YongshengORCID,Qin Sujuan,Shi Yijie,Wen Qiaoyan

Abstract

Software-defined networking (SDN) is a new networking paradigm that realizes the fast management and optimal configuration of network resources by decoupling control logic and forwarding functions. However, centralized network architecture brings new security problems, and denial-of-service (DoS) attacks are among the most critical threats. Due to the lack of an effective message-verification mechanism in SDN, attackers can easily launch a DoS attack by faking the source address information. This paper presents DoSGuard, an efficient and protocol-independent defense framework for SDN networks to detect and mitigate such attacks. DoSGuard is a lightweight extension module on SDN controllers that mainly consists of three key components: a monitor, a detector, and a mitigator. The monitor maintains the information between the switches and the hosts for anomaly detection. The detector utilizes OpenFlow message and flow features to detect the attack. The mitigator protects networks by filtering malicious packets. We implement a prototype of DoSGuard in the floodlight controller and evaluate its effectiveness in a simulation environment. Experimental results show the DoSGuard achieves 98.72% detecion precision, and the average CPU utilization of the controller is only around 8%. The results demonstrate that DoSGuard can effectively mitigate DoS attacks against SDN with limited overhead.

Funder

National Natural Science Foundation of China

Key Project Plan of Blockchain in Ministry of Education of the People's Republic of China

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry

Cited by 12 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. A DoS attack detection method based on adversarial neural network;PeerJ Computer Science;2024-08-09

2. Denial of Service Attack Detection and Mitigation using Ensemble based ML in Software Defined Network;2024 3rd International Conference on Applied Artificial Intelligence and Computing (ICAAIC);2024-06-05

3. Abnormal traffic detection system in SDN based on deep learning hybrid models;Computer Communications;2024-02

4. SynFloWatch: A Detection System against TCP-SYN based DDoS Attacks using Entropy in Hybrid SDN;Proceedings of the 25th International Conference on Distributed Computing and Networking;2024-01-04

5. A Compressive Study on Detection Accuracy Model for DoS Attack in SDN Using Ensemble Learning Techniques;2023 7th International Symposium on Innovative Approaches in Smart Technologies (ISAS);2023-11-23

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