DDOS detection and denial using third party application in SDN

Thomas, Roshni Mary; James, Divya

doi:10.1109/icecds.2017.8390193

Cited by 13 publications

(11 citation statements)

References 3 publications

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“…The experiment was carried out in real environment using 5 PCs that had Ubuntu 18.04 installed and applied a tree topology to the network architecture design consisting of 1 RYU controller [20], 3 Mikrotik switches [21] and 4 hosts with Core i3 4GB RAM specifications. In addition, the protocol used to connect controllers and switches is OpenFlow [4]. The topology is depicted in Figure 1.…”

Section: Methodsmentioning

confidence: 99%

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SDN-Honeypot Integration for DDoS Detection Scheme Using Entropy

Kilwalaga

Sumadi

Syaifuddin

2020

KINETIK

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Limitations on traditional networks contributed to the development of a new paradigm called Software Defined Network (SDN). The separation of control and data plane provides an advantage as well as a security gap on the SDN network because all controls are centralized on the controller so when the compilation of attacks are directed the controller, the controller will be overburdened and eventually dropped. One of the attacks that can be used is the DDoS attack - ICMP Flood. ICMP Flood is an attack intended to overwhelm the target with a large number of ICMP requests. To overcome this problem, this paper proposes detection and mitigation using the Modern Honey Network (MHN) integration in SDN and then makes reactive applications outside the controller using the entropy method. Entropy is a statistical method used to calculate the randomness level of an incoming packet and use header information as a reference for its calculation. In this study, the variables used are the source of IP, the destination of IP and protocol. The results show that detection and mitigation were successfully carried out with an average value of entropy around 10.830. Moreover, CPU usage either in normal packet delivery or attacks showed insignificant impact from the use of entropy. In addition, it can be concluded that the best data collected in 30 seconds in term of the promptness of mitigation flow installation.

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Section: Methodsmentioning

confidence: 99%

“…In SDN there are three infrastructure layers, which in the control and data layer are connected using the southbound interface, namely OpenFlow. OpenFlow itself is one of the protocol standards in the SDN network that is used to communicate securely [4]. However, it does not prevent an occurrence of vulnerability.…”

Section: Introductionmentioning

confidence: 99%

SDN-Honeypot Integration for DDoS Detection Scheme Using Entropy

Kilwalaga

Sumadi

Syaifuddin

2020

KINETIK

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“…The controller layer contains configurations based on rules that have been made which will be applied in the data plane. Whereas in the data plane consists of network devices such as routers and switches that are used to forward data packets [8]. SDN architecture is divided into three layers, namely the application layer, the control layer, and the infrastructure layer [4] [7].…”

Section: Introductionmentioning

confidence: 99%

“…The controller layer contains a configuration based on rules that have been created that will be applied to data fields. Whereas in the field of data consists of network devices such as routers and switches that are used to forward data packets [8]. To connect between layers using an API consisting of two types, namely the API to the north to connect the control layer with the application layer and the API to the south is used to connect the control layer with the infrastructure layer [9].…”

Section: Introductionmentioning

confidence: 99%

Detection and Mitigation of Distributed Denial of Service Attacks on Network Architecture Software Defined Networking Using the Naive Bayes Algorithm

Munir¹,

Ardiansyah²,

Santoso³

et al. 2022

JOISM

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DDoS attacks are a form of attack carried out by sending packets continuously to machines and even computer networks. This attack will result in a machine or network resources that cannot be accessed or used by users. DDoS attacks usually originate from several machines operated by users or by bots, whereas Dos attacks are carried out by one person or one system. In this study, the term to be used is the term DDoS to represent a DoS or DDoS attack. In the network world, Software Defined Network (SDN) is a promising paradigm. SDN separates the control plane from forwarding plane to improve network programmability and network management. As part of the network, SDN is not spared from DDoS attacks. In this study, we use the naïve Bayes algorithm as a method to detect DDoS attacks on the Software Defined Network network architecture

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“…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. DDoS detection method checks the incoming traffic and analyzes it.…”

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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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DDOS detection and denial using third party application in SDN

Cited by 13 publications

References 3 publications

SDN-Honeypot Integration for DDoS Detection Scheme Using Entropy

SDN-Honeypot Integration for DDoS Detection Scheme Using Entropy

Detection and Mitigation of Distributed Denial of Service Attacks on Network Architecture Software Defined Networking Using the Naive Bayes Algorithm

Ddos Attack Detection and Mitigation at SDN Enviroment

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