Summary Software‐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.

Abstract A distributed denial of service (DDoS) attack is a significant threat to web‐based applications and hindering legitimate traffic (denies access to benign users) by overwhelming the victim system or its infrastructure (service, bandwidth, networking devices, etc.) with a large volume of attack traffic. It leads to a delay in responses or sometimes a crash victim system. Even a few moments of pause in web‐based applications lead to a huge monetary loss and a bad reputation in the market. Several approaches available in the literature to protect websites from different types of DDoS attacks. However, incidents and volume sizes of DDoS attacks are growing quarter by quarter. Further, various challenges in the traditional framework based defense mechanisms: itself becoming a victim of attacks while analyzing a massive amount of traffic, require more time for detection process, no coordination among the modules, etc. This paper presents a comprehensive DDoS defense deployment taxonomy and critically reviewed existing distributed frameworks based DDoS attack detection systems. Further, characterized several existing distributed processing frameworks to select an appropriate one for deploying DDoS attack detection mechanisms. Finally, several evaluation metrics, open issues, discussion on available datasets including their limitations, and future directions are presented.