DDoS attack detection scheme based on entropy and PSO-BP neural network in SDN

Liu, Zhenpeng; He, Yupeng; Wang, Wensheng; Bin, Zhang

doi:10.23919/jcc.2019.07.012

Cited by 58 publications

(19 citation statements)

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“…Solutions of artificial neural networks (ANN) simulate the human brain structure to abstract knowledge through automatic learning for flow identification [26]. Hannache [27] proposed a Neural Network based Traffic Flow Classifier (TFC-NN) to detect DDoS attacks in the SDN environment.…”

Section: Related Workmentioning

confidence: 99%

DDoS Detection Using a Cloud-Edge Collaboration Method Based on Entropy-Measuring SOM and KD-Tree in SDN

Yu²,

Hong

et al. 2021

Security and Communication Networks

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Software-defined networking (SDN) emerges as an innovative network paradigm, which separates the control plane from the data plane to improve the network programmability and flexibility. It is widely applied in the Internet of Things (IoT). However, SDN is vulnerable to DDoS attacks, which can cause network disasters. In order to protect SDN security, a DDoS detection method using cloud-edge collaboration based on Entropy-Measuring Self-organizing Maps and KD-tree (EMSOM-KD) is designed for SDN. Entropy measurement is utilized to select the ideal SOM map and classify SOM neurons considering the limitation of dead and suspicious neurons. EMSOM can detect most flows directly and filter out a few doubtable flows. Then these flows are fine-grained, identified by KD-tree. Due to the limited and precious resources of the controller, parameter computation is performed in the cloud. The edge controller implements DDoS detection by EMSOM-KD. The experiments are conducted to evaluate the performance of the proposed method. The results show that EMSOM-KD has better detection accuracy; moreover, it improves the KD-tree detection efficiency.

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Section: Related Workmentioning

confidence: 99%

DDoS Detection Using a Cloud-Edge Collaboration Method Based on Entropy-Measuring SOM and KD-Tree in SDN

Yu²,

Hong

et al. 2021

Security and Communication Networks

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show abstract

“…However, the authors must validate the proposed approach with recent real traffic datasets and improve the detection method to identify different types of DDoS attacks. Liu et al [74] also proposed a DDoS detection method in the context of an SDN. The proposed method deploys an entropy approach on the switch, which results in a distinction being made between normal and abnormal traffic.…”

Section: B Ddos Defense Systems Based On ML Techniques In Sdn Enviromentioning

confidence: 99%

Machine Learning Approaches for Combating Distributed Denial of Service Attacks in Modern Networking Environments

Aljuhani

2021

IEEE Access

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“…Nowadays, neural networks have made a comeback and are playing an increasingly important role in machine learning as a new approach. Liu et al [13] designed a DDoS attack detection scheme based on the combination of generalized information entropy and BP neural network in SDN environment and used the particle swarm optimization algorithm to optimize the BP neural network-related parameters and improve the detection ability. Tang et al [14] introduced a SDN intrusion monitoring system based on Gate Recurrent Unit-Recurrent Neural Network (RNN), which achieved an accuracy of 89% with 6 original features.…”

Section: Related Workmentioning

confidence: 99%

A cooperative DDoS attack detection scheme based on entropy and ensemble learning in SDN

Zhang

et al. 2021

J Wireless Com Network

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In order to solve the problem of distributed denial of service (DDoS) attack detection in software-defined network, we proposed a cooperative DDoS attack detection scheme based on entropy and ensemble learning. This method sets up a coarse-grained preliminary detection module based on entropy in the edge switch to monitor the network status in real time and report to the controller if any abnormality is found. Simultaneously, a fine-grained precise attack detection module is designed in the controller, and a ensemble learning-based algorithm is utilized to further identify abnormal traffic accurately. In this framework, the idle computing capability of edge switches is fully utilized with the design idea of edge computing to offload part of the detection task from the control plane to the data plane innovatively. Simulation results of two common DDoS attack methods, ICMP and SYN, show that the system can effectively detect DDoS attacks and greatly reduce the southbound communication overhead and the burden of the controller as well as the detection delay of the attacks.

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DDoS attack detection scheme based on entropy and PSO-BP neural network in SDN

Cited by 58 publications

References 0 publications

DDoS Detection Using a Cloud-Edge Collaboration Method Based on Entropy-Measuring SOM and KD-Tree in SDN

DDoS Detection Using a Cloud-Edge Collaboration Method Based on Entropy-Measuring SOM and KD-Tree in SDN

Machine Learning Approaches for Combating Distributed Denial of Service Attacks in Modern Networking Environments

A cooperative DDoS attack detection scheme based on entropy and ensemble learning in SDN

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