Development of Software-Defined Mesh Network Emulator Testbed for DDoS Defence Study

Sirijaroensombat, Sumeth; Nangsue, Col. Phaderm; Aswakul, Chaodit

doi:10.1109/ccoms.2019.8821667

Cited by 4 publications

(5 citation statements)

References 8 publications

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“…Vol. [11] is the most similar to this project idea. with different in testbed components, its use GNS3 emulator and VMware where this paper use Mininet support OpenFlow for highly flexible custom routing and Software-Defined Networking.…”

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confidence: 83%

“…The paper contains a weak attack scenario, the flow entries for the packets which are directly installed by the controller are classified as attack traffic whereas the flow table entries for those packets which are requested by the switch are grouped under genuine traffic, by manipulating with "TIMEOUT" parameter value without SDN application. [11] In this paper, the network constructs consist of virtual Iraqi Journal of Information and Communications Technology(IJICT)…”

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confidence: 99%

“…with different in testbed components, its use GNS3 emulator and VMware where this paper use Mininet support OpenFlow for highly flexible custom routing and Software-Defined Networking. The network topology of [11] does not customize like this paper topology its only simple mesh topology. The weakness in paper [11], it already knows the attacker IP while this paper finds and extracts the attackers from the traffic toward the victim.…”

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confidence: 99%

“…The network topology of [11] does not customize like this paper topology its only simple mesh topology. The weakness in paper [11], it already knows the attacker IP while this paper finds and extracts the attackers from the traffic toward the victim. In [11] the dropping rule send to all the switches in the network so the user will be denied from all the other network, while this project perform rule sending to specific switch, in [11] the script work manually so there is no time for mitigation can be evaluated .…”

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confidence: 99%

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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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confidence: 83%

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confidence: 99%

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confidence: 99%

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confidence: 99%

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Ddos Attack Detection and Mitigation at SDN Enviroment

Abdulkarem¹,

Alethawy²

2021

Iraqi Journal of ICT

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“…The structural characteristics of the controller will make it vulnerable to DDoS attacks. Then, referring to the simulation tools [26], we use GNS3 for emulating network topology, ONOS [27] as a controller to build our system model, and use Hping3, SlowLoris, and GoldenEye for performing an attack, and Cisco TRex [28] for generating benign traffic. SFlow-RT collects the sampling flow traffic as the sFlow Collector, while Prometheus will get and save the data from sFlow-RT.…”

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confidence: 99%

XGBoost Classifier for DDOS Attack Detection in Software Defined Network Using sFlow Protocol

Rozam¹,

Riasetiawan²

2023

Int. J. Adv. Sci. Eng. Inf. Technol.

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From a security perspective, Software Defined Network (SDN) separates security concerns into Control Plane and Data Plane. The Control Plane is responsible for managing the entire network centrally. Centralized SDN generates high vulnerability against the Distributed Denial of Service (DDOS). When the Software Defined Network overwhelms by DDOS, both Control Plane and Data Plane will lack resources. It can cause the SDN to fail to work if not detected early. Using the ability of sFlow Protocol to capture the flow traffic in real time, the data could be used to detect DDOS attacks. This sFlow sampling approach can reduce the workload of the network by lower down the processing in switches. This paper uses Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), and Random Forest as detection methods. We use ONOS as SDN Controller and build the topology in GNS3. Prometheus retrieves data from the sFlow Collector as a time series database. The classifier then uses the data from Prometheus for DDOS detection. For the dataset, we use four different datasets. Datasets 1 and 2 consist of 6109 data, each divided into two classes and three classes. Datasets 3 and 4 consist of 400488 data divided into 2 and 3 classes, respectively. The evaluation results have proved the effectiveness of the proposed method. XGBoost has the highest accuracy of another algorithm. The best accuracy is 99.84% using Dataset 4 as the training set.

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DDoS Attack Detection and Mitigation at SDN Data Plane Layer

Abdulkarem¹,

Dawod

2020

2020 2nd Global Power, Energy and Communication Conference (GPECOM)

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Development of Software-Defined Mesh Network Emulator Testbed for DDoS Defence Study

Cited by 4 publications

References 8 publications

Ddos Attack Detection and Mitigation at SDN Enviroment

Ddos Attack Detection and Mitigation at SDN Enviroment

XGBoost Classifier for DDOS Attack Detection in Software Defined Network Using sFlow Protocol

DDoS Attack Detection and Mitigation at SDN Data Plane Layer

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