A Study of Distributed SDN Controller Based on Apache Kafka

Moon, Ju-Hyeon; Shine, Yong-Tae

doi:10.1109/bigcomp48618.2020.0-101

Cited by 10 publications

(3 citation statements)

References 1 publication

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“…It works based on the publish-subscribe model, where producers publish to a distributed queue and consumers subscribe to obtain the data when available. The data streaming component (queuing module) of our architecture relies on Apache Kafka [11], a community project maintained by the Apache software foundation.…”

Section: Data Streamingmentioning

confidence: 99%

Parameterization and Performance Analysis of a Scalable, near Real-Time Packet Capturing Platform

Oliveira,

Pedrosa,

Rufino

et al. 2024

Systems

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The rapid evolution of technology has fostered an exponential rise in the number of individuals and devices interconnected via the Internet. This interconnectedness has prompted companies to expand their computing and communication infrastructures significantly to accommodate the escalating demands. However, this proliferation of connectivity has also opened new avenues for cyber threats, emphasizing the critical need for Intrusion Detection Systems (IDSs) to adapt and operate efficiently in this evolving landscape. In response, companies are increasingly seeking IDSs characterized by horizontal, modular, and elastic attributes, capable of dynamically scaling with the fluctuating volume of network data flows deemed essential for effective monitoring and threat detection. Yet, the task extends beyond mere data capture and storage; robust IDSs must integrate sophisticated components for data analysis and anomaly detection, ideally functioning in real-time or near real-time. While Machine Learning (ML) techniques present promising avenues for detecting and mitigating malicious activities, their efficacy hinges on the availability of high-quality training datasets, which in turn poses a significant challenge. This paper proposes a comprehensive solution in the form of an architecture and reference implementation for (near) real-time capture, storage, and analysis of network data within a 1 Gbps network environment. Performance benchmarks provided offer valuable insights for prototype optimization, demonstrating the capability of the proposed IDS architecture to meet objectives even under realistic operational scenarios.

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Section: Data Streamingmentioning

confidence: 99%

Parameterization and Performance Analysis of a Scalable, near Real-Time Packet Capturing Platform

Oliveira,

Pedrosa,

Rufino

et al. 2024

Systems

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show abstract

“…AMQP-based SDN middleware systems are often used to build distributed control plane. In [40], such a middleware has been considered that augments Rab-bitMQ and ActiveMQ with AMQP for supporting reliable message communication among the controllers. Similarly, in [41], another AMQP middleware is modelled to exchange information regarding network bandwidth, network topologies and inter-connected nodes among the distributed controllers.…”

Section: • Advanced Message Queuing Protocol (Amqp)mentioning

confidence: 99%

Software-Defined Multi-domain Tactical Networks: Foundations and Future Directions

Mahmud¹,

Toosi²,

Rodríguez³

et al. 2020

Preprint

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Software Defined Networking (SDN) has emerged as a programmable approach for provisioning and managing network resources by defining a clear separation between the control and data forwarding planes. Nowadays SDN has gained significant attention in the military domain. Its use in the battlefield communication facilitates the end-to-end interactions and assists the exploitation of edge computing resources for processing data in the proximity. However, there are still various challenges related to the security and interoperability among several heterogeneous, dynamic, intermittent, and data packet technologies like multi-bearer network (MBN) that need to be addressed to leverage the benefits of SDN in tactical environments. In this chapter, we explicitly analyse these challenges and review the current research initiatives in SDN-enabled tactical networks. We also present a taxonomy on SDNbased tactical network orchestration according to the identified challenges and map the existing works to the taxonomy aiming at determining the research gaps and suggesting future directions.

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“…The control plane is entirely configurable and can possess the potential capabilities to integrate interloper networks such as IoT with the data plane. The data plane then ensures a smooth flow of data across both participants under the regulations of the control plane [15]. The control plane in other words is capable to control the inner communicational infrastructure of IoT by taking a pilot control over the assemblage system.…”

Section: Introductionmentioning

confidence: 99%

Cyber Threats Detection in Smart Environments Using SDN-Enabled DNN-LSTM Hybrid Framework

et al. 2022

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Internet of Things (IoT) is an instantly exacerbated communication technology that is manifesting miraculous effectuation to revolutionize conventional means of network communication. The applications of IoT are compendiously encompassing our prevalent lifestyle and the integration of IoT with other technologies makes this application spectrum even more latitudinous. However, this admissibility also introduces IoT with a pervasive array of imperative security hazards that demands noteworthy solutions to be swamped. In this scientific study, we proposed Deep Learning (DL) driven Software Defined Networking (SDN) enabled Intrusion Detection System (IDS) to combat emerging cyber threats in IoT. Our proposed model (DNNLSTM) is capable to encounter a tremendous class of common as well as less frequently occurring cyber threats in IoT communications. The proposed model is trained on CICIDS 2018 dataset, and its performance is evaluated on several decisive parameters i.e Accuracy, Precision, Recall, and F1-Score. Furthermore, the designed framework is analytically compared with relevant classifiers, i.e., DNNGRU, and BLSTM for appropriate validation. An exhaustive performance comparison is also conducted between the proposed system and a few preeminent solutions from the literature. The proposed design has circumvented the existing literature with unprecedented performance repercussions such as 99.55% accuracy, 99.36% precision, 99.44% recall, and 99.42% F1-score.

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A Study of Distributed SDN Controller Based on Apache Kafka

Cited by 10 publications

References 1 publication

Parameterization and Performance Analysis of a Scalable, near Real-Time Packet Capturing Platform

Parameterization and Performance Analysis of a Scalable, near Real-Time Packet Capturing Platform

Software-Defined Multi-domain Tactical Networks: Foundations and Future Directions

Cyber Threats Detection in Smart Environments Using SDN-Enabled DNN-LSTM Hybrid Framework

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