IQR-based approach for DDoS detection and mitigation in SDN

Swami, Rochak; Dave, Mayank; Ranga, Virender

doi:10.1016/j.dt.2022.10.006

Cited by 6 publications

(7 citation statements)

References 19 publications

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“…• Several systems 45,46,50,52 are available in the literature to protect the SDN environment from different types of DDoS attacks. However, most of them failed during large-scale DDoS attacks.…”

Section: Discussionmentioning

confidence: 99%

“…We characterize and compare the proposed K-DDoS-SDN with existing works. 5,9,10,27,28,[30][31][32][33][34][36][37][38][44][45][46][47]56 DSPF-based approaches, 5,9,10,[30][31][32]38,43,56 for DDoS attacks deployed on the Spark and Kafka framework. This type of mechanism analyzes network streams in micro-batch processing mode (near-to-live).…”

Section: Characterization With Existing Workmentioning

confidence: 99%

“…However, their primary focus is to protect the Internet-based services and failed to protect the SDN environment from DDoS attacks. Further, some [44][45][46][47] researchers proposed mechanisms to protect the SDN environment or controller from different DDoS attacks. However, they failed to analyze network traces in real-time and not provided distributed solution.…”

Section: Characterization With Existing Workmentioning

confidence: 99%

“…Further, a few researchers, [44][45][46][47][48][49][50][51][52][53] proposed various detection approaches to protect SDN controller, environment, or topology from different types of DDoS attacks. However, most of the systems are deployed on TF.…”

Section: Introductionmentioning

confidence: 99%

“…Further, it is challenging to provide dynamic thresholds in the production environment. Several existing TF and DSPF‐based state‐of‐arts 30,34‐36 implemented and evaluated using outdated datasets. Hence, there is a need for detection mechanisms implemented and validated using the recent dataset (“CICDDoS2019 dataset”). Several systems 45,46,50,52 are available in the literature to protect the SDN environment from different types of DDoS attacks. However, most of them failed during large‐scale DDoS attacks. This research article provides a distributed and real‐time DDoS attack detection approach for protecting the SDN environment from large‐scale DDoS attacks.…”

Section: Introductionmentioning

confidence: 99%

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K‐DDoS‐SDN: A distributed DDoS attacks detection approach for protecting SDN environment

Kaur,

Krishna,

Patil

2023

Concurrency and Computation

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SummarySoftware‐defined networking (SDN) is an advanced networking paradigm that decouples forwarding control logic from the data plane. Therefore, it provides a loosely‐coupled architecture between the control and data plane. This separation provides flexibility in the SDN environment for addressing any transformations. Further, it delivers a centralized way of managing networks due to control logic embedded in the SDN controller. However, this advanced networking paradigm has been facing several security issues, such as topology spoofing, exhausting bandwidth, flow table updating, and distributed denial of service (DDoS) attacks. A DDoS attack is one of the most powerful menaces to the SDN environment. Further, the central data controller of SDN becomes the primary target of DDoS attacks. In this article, we propose a Kafka‐based distributed DDoS attacks detection approach for protecting the SDN environment named K‐DDoS‐SDN. The K‐DDoS‐SDN consists of two modules: (i) Network traffic classification (NTClassification) module and (ii) Network traffic storage (NTStorage) module. The NTClassification module is the detection approach designed using scalable H2O ML techniques in a distributed manner and deployed an efficient model on the two‐nodes Kafka Streams cluster to classify incoming network traces in real‐time. The NTStorage module collects raw packets, network flows, and 21 essential attributes and then systematically stores them in the HDFS to re‐train existing models. The proposed K‐DDoS‐SDN designed and evaluated using the recent and publically available CICDDoS2019 dataset. The average classification accuracy of the proposed distributed K‐DDoS‐SDN for classifying network traces into legitimate and one of the most popular attacks, such as DDoS_UDP is 99.22%. Further, the outcomes demonstrate that proposed distributed K‐DDoS‐SDN classifies traffic traces into five categories with at least 81% classification accuracy.

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Section: Discussionmentioning

confidence: 99%