The Design of SDN Based Detection for Distributed Denial of Service (DDoS) Attack

Oo, Myo Minn; Kamolphiwong, Sinchai; Kamolphiwong, Thossaporn

doi:10.1109/icsec.2017.8443939

Cited by 13 publications

(11 citation statements)

References 4 publications

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“…Siddharth and Sterbenz [104] extends the functionality of the SDN controller to include a resilience framework, ReSDN incorporates ML to distinguish DoS attacks. And the advanced SVM (ASVM) [105] was proposed based on SDN for DDoS attacks. It significantly reduces the testing time as well as training time compared with the traditional SVM algorithm.…”

Section: ) Network Attacks Detectionmentioning

confidence: 99%

A Survey of Networking Applications Applying the Software Defined Networking Concept Based on Machine Learning

et al. 2019

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The main task of future networks is to build, as much as possible, intelligent networking architectures for intellectualization, activation, and customization. Software-defined networking (SDN) technology breaks the tight coupling between the control plane and the data plane in the traditional network architecture, making the controllability, security, and economy of network resources into a reality. As one of the important actualization methods of artificial intelligence (AI), machine learning (ML), combined with SDN architecture will have great potential in areas, such as network resource management, route planning, traffic scheduling, fault diagnosis, and network security. This paper presents the network applications combined with SDN concepts based on ML from two perspectives, namely the perspective of ML algorithms and SDN network applications. From the perspective of ML algorithms, this paper focuses on the applications of classical ML algorithms in SDN-based networks, after a characteristic analysis of algorithms. From the other perspective, after classifying the existing network applications based on the SDN architecture, the related ML solutions are introduced. Finally, the future development of the ML algorithms and SDN concepts is discussed and analyzed. This paper occupies the intersection of the AI, big data, computer networking, and other disciplines; the AI itself is a new and complex interdisciplinary field, which causes the researchers in this field to often have different professional backgrounds and, sometimes, divergent research purposes. This paper is necessary and helpful for researchers from different fields to accurately master the key issues.INDEX TERMS Artificial intelligence, machine learning, network management, software-defined networking.

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Section: ) Network Attacks Detectionmentioning

confidence: 99%

A Survey of Networking Applications Applying the Software Defined Networking Concept Based on Machine Learning

et al. 2019

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“…From Table 1, we have found that there are very few applications created for ONOS controllers for the detection and mitigation of DDoS attack. Chen et al 8 and Oo et al 9 both have used ONOS controller for their work, but the former work does not have an application for detection and mitigation. In contrast, the latter work has only a detection application created.…”

Section: Introductionmentioning

confidence: 99%

ONOS Flood Defender: An Intelligent Approach to Mitigate DDoS Attack in SDN

Aslam

Srivastava

Gore

2022

Trans Emerging Tel Tech

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Software-Defined Networking (SDN) has made its place in the networks as new technology. SDN's programmable behavior enables it to change behavior on the fly, provides instructions for the task's automatic performance, dynamic scaling, and service integration. These advantages have made SDN necessary in networks. However, SDN suffers from the threat of DDoS attack. We have developed an approach to mitigate these threats by creating an ONOS Flood Defender Application (OFD App). This app effectively detects DDoS attack using supervised and ensemble machine learning techniques and mitigates them by tracebacking the attack traffic to its origin. Our results show that ensemble machine learning techniques perform better than single machine learning algorithm to detect DDoS attack. Random Forest classifier (RFC), an ensemble technique, performs best with the highest accuracy of 99.3% in detecting DDoS attack, followed by XGBoost classifier with an accuracy of 99%. The proposed framework of the OFD app is implemented in a Mininet emulator and ONOS SDN controller. The application performs effectively in terms of time, accuracy, and overhead of the system. Our app efficiently mitigates the attacks, thereby preventing a tremendous amount of damage to legitimate users.

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“…The methods capture the flow volume feature as well as the flow rate asymmetry feature, to adaptively change the flow monitoring granularities on all switches to quickly locate the potential victims and suspicious attackers. [8] In this paper, propose to use Advanced Support Vector Machine (ASVM) algorithm in order to detect DDoS attacks.…”

mentioning

confidence: 99%

“…Both papers in [2] and [8] talk only about DDoS detection by using POX and ONOS controllers. In [9], a firewall will block the addresses forwarded by the server.…”

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confidence: 99%

Ddos Attack Detection and Mitigation at SDN Enviroment

Abdulkarem¹,

Alethawy²

2021

Iraqi Journal of ICT

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Abstract- Software-Defined Networking (SDN) is a promising sample that allows the programming behind the network’s operation with some abstraction level from the underlying networking devices .the insistence to detect and mitigate Distributed Denial of Service (DDoS) which introduced by network devices tries to discover network security weaknesses and the negative effects of some types of Distributed Denial of Service (DDoS) attacks. An SDN-based generic solution to mitigate DDoS attacks when and where they originate. Briefly, it compares at runtime the expected trend of normal traffic against the trend of abnormal traffic; if big deviation on the traffic trend is detected, then an event is created; as an event associated to a DDoS attack is produced, an SDN (OpenDayLight) controller creates flow rules for blocking the malign traffic, By designing and implementing an application that reactively impairs the attacks at its origin, ensuring the “normal operation” of the network infrastructure. The evaluation results suggest that the proposal timely detect the characteristics of a flooding DDoS attacks, and mitigates their negative impacts on the network performance, and ensures the correct data delivery of normal traffic. The work sheds light on the programming relevance over an abstracted view of the network infrastructure.

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The Design of SDN Based Detection for Distributed Denial of Service (DDoS) Attack

Cited by 13 publications

References 4 publications

A Survey of Networking Applications Applying the Software Defined Networking Concept Based on Machine Learning

A Survey of Networking Applications Applying the Software Defined Networking Concept Based on Machine Learning

ONOS Flood Defender: An Intelligent Approach to Mitigate DDoS Attack in SDN

Ddos Attack Detection and Mitigation at SDN Enviroment

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