Distributed Denial of Service Defense in Software Defined Network Using OpenFlow

Zhai, P; Song, Yanbo; Zhu, Xiaoming; Cao, Lihui; Zhang, Jiaming; Yang, Chungang

doi:10.1109/iccc49849.2020.9238872

Cited by 9 publications

(2 citation statements)

References 17 publications

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“…For example, one of the largest attacks was in February 2018 on the GitHub site, which is a well-known code hosting site. This attack resulted in stopping the service for several hours by sending huge traffic that reached 1.35 Tb/s [3]. Another big attack happened last year in November on the Azure Microsoft site in Asia, which is a well-known cloud computing site.…”

Section: Introductionmentioning

confidence: 99%

Q-learning based distributed denial of service detection

Yaseen

Al-Saadi

2023

IJECE

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<span lang="EN-US">Distributed denial of service (DDoS) attacks the target service providers by sending a huge amount of traffic to prevent legitimate users from getting the service. These attacks become more challenging in the software-defined network paradigm, due to the separation of the control plane from the data plane. Centralized software defined networks are more vulnerable to DDoS attacks that may cause the failure of all networks. In this work, a new approach is proposed based on q-learning to enhance the detection of DDoS attacks and reduce false positives and false negatives. The results of this work are compared with entropy detection in terms of the number of received packets to detect the attack and also the continuity of service for legitimate users. Moreover, these results indicate that the proposed system detects the DDoS attack from flash crowds and redirects the traffic to the edge of the data center. A second controller is used to redirect traffic to a honeypot server that works as a mirror server. This guarantees the continuity of service for both normal and suspected traffic until further analysis is done. The results indicate an increase of up to 50% in the throughput compared to other approaches.</span>

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Section: Introductionmentioning

confidence: 99%

Q-learning based distributed denial of service detection

Yaseen

Al-Saadi

2023

IJECE

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“…Therefore, a new paradigm appeared which is known as Software Defined Network (SDN). SDN separates the control plane from the data plane to create centralized control which is more dynamic, flexible, manageable, and adaptable [2,3]. The control plane contains the routing algorithm that determines the path from source to destination.…”

Section: Introductionmentioning

confidence: 99%

Load Balancing and Detection of Distributed Denial of Service Attacks Using Entropy Detection

2021

IJCCCE

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Software Defined Network (SDN) is a modern network architecture that has a centralized controller. It is more flexible, and programmable due to the separation of the control plane from the data plane. However, Distributed Denial of Service (DDoS) attacks is one of the dangers that the SDN network is facing. It could attack and stop the controller from working, causing the whole system to be down. Moreover, DDoS attacks can target the hosts and the switches to stop the services for a long time as they could cause more damage to the network or datacenter. In this work, a proposed approach is utilized to protect datacenter networks and servers from DDoS attacks using entropy and real SDN-controller Python Network Operating system (POX) by redirect traffic to the edge of the datacenter to minimize the damage. The results of this experiment show how to detect abnormal traffics in an early stage and isolate them in a server outside the datacenter to distribute the huge amount of traffic in more than one server and avoid congestion on switches. Also, the throughput of the server was increased by about %16 during the suspected attack, this means maintaining the service until further analysis to be done on the traffic. These results are compared with the direct block mitigation method which was mostly used with the entropy detection method in previous researches. Moreover, this work is done to confirm whether the suspected traffic is an actual attack or not. Therefore, this method will decrease the false positives of detection. IndexTerms— Software-Defined Networking (SDN), Distributed Denial of Service (DDoS) attacks, OpenFlow, Python Network Operating system (POX).

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DeMi: A Solution to Detect and Mitigate DoS Attacks in SDN

Eliyan

Pietro

2023

IEEE Access

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Distributed Denial of Service Defense in Software Defined Network Using OpenFlow

Cited by 9 publications

References 17 publications

Q-learning based distributed denial of service detection

Q-learning based distributed denial of service detection

Load Balancing and Detection of Distributed Denial of Service Attacks Using Entropy Detection

DeMi: A Solution to Detect and Mitigate DoS Attacks in SDN

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