Next Generation Data Link for IRSP Systems

May, Daniel; Landwehr, Aaron; Browning, Tyler; Cotton, Chase; Kiamilev, Fouad

doi:10.1109/rapid51799.2021.9521371

Cited by 1 publication

(1 citation statement)

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“…However, the increased efficiency that these new networks create is largely due to the number of intelligent devices supported and the related applications. Broad-bandwidth data links: intelligent application deployments require data links capable of a minimum of 25 Mbps and are designed to sustain meaningful augmented reality (AR) and virtual reality (VR) and data containers [3]. Large scale and structurally flexible networks: This is defined by the network function virtualization (NFV) mechanism to create the networks needed.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Intrusion Detection and Prevention System for 5G and beyond Software-Defined Networks

Bocu

Явич

2022

Symmetry

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The philosophy of the IoT world is becoming important for a projected, always-connected world. The 5G networks will significantly improve the value of 4G networks in the day-to-day world, making them fundamental to the next-generation IoT device networks. This article presents the current advances in the improvement of the standards, which simulate 5G networks. This article evaluates the experience that the authors gained when implementing Vodafone Romania 5G network services, illustrates the experience gained in context by analyzing relevant peer-to-peer work and used technologies, and outlines the relevant research areas and challenges that are likely to affect the design and implementation of large 5G data networks. This paper presents a machine learning-based real-time intrusion detection system with the corresponding intrusion prevention system. The convolutional neural network (CNN) is used to train the model. The system was evaluated in the context of the 5G data network. The smart intrusion detection system (IDS) takes the creation of software-defined networks into account. It uses models based on artificial intelligence. The system is capable to reveal not previously detected intrusions using software components based on machine learning, using the convolutional neural network. The intrusion prevention system (IPS) blocks the malicious traffic. This system was evaluated, and the results confirmed that it provides higher efficiencies compared to less overhead-like approaches, allowing for real-time deployment in 5G networks. The offered system can be used for symmetric and asymmetric communication scenarios.

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Section: Introductionmentioning

confidence: 99%