ADT: AI-Driven network Telemetry processing on routers

Foroughi, Parisa; Brockners, Frank; Rougier, Jean-Louis

doi:10.1016/j.comnet.2022.109474

Cited by 2 publications

(2 citation statements)

References 33 publications

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“…With artificial intelligence, failure detection methods based on machine learning have been introduced [21]. Hou et al [22] use an unsupervised two-stage method to detect and characterize the faults in networks.…”

Section: Related Workmentioning

confidence: 99%

Efficient end-to-end failure probing matrix construction in data center networks

Jia,

Liu,

et al. 2023

J. Commun. Netw.

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Data centers play an essential role in the functioning of modern society. However, failures are unavoidable in data center networks (DCN) and will lead to negative impact on all applications. Therefore, researchers are interested in the rapid detection and localization of failures in DCNs.In this paper, we present a theoretical model to analyze the end-to-end failure detection methods in data center networks. Our numerical results verify that the proposed theoretical model is accurate. In addition, we propose an algorithm to construct probing matrices based on an enhanced probing path selection indicator. We also introduce deep reinforcement learning (DRL) method to solve the problem and propose a DRL-based probing matrix construction algorithm. Our experimental results show that both of the proposed algorithms for constructing probing matrices achieve better performance in detection accuracy than existing methods. We discussed different scenarios that the algorithms are applicable to that can improve detection accuracy or construction speed performance.

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Section: Related Workmentioning

confidence: 99%

Efficient end-to-end failure probing matrix construction in data center networks

Jia,

Liu,

et al. 2023

J. Commun. Netw.

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“…The IIoT revolution is rapidly expanding, leading to significant financial advantages and automation. However, the IoMT has also expanded to become a multimillion-dollar sector (Foroughi et al 2023). The open and ubiquitous nature of the IoMT ecosystem makes it a potential target for numerous new cyber threats and assaults even while it offers substantial advantages.…”

mentioning

confidence: 99%

Revolutionizing network management with an AI-driven intrusion detection system

Vijay,

Sharma,

Khanna

2023

Multidiscip. Sci. J.

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The creation of methods and models that can learn and make predictions or judgments based on such learning is artificial intelligence (AI). By combining an AI-driven intrusion detection system (IDS) with the BAT optimization method and a Deep Convolutional Neural Network (DCNN), we provide a revolutionary strategy to the revolutionize network management. Utilizing the advantages of deep learning and BAT optimization, the goal is to increase the efficiency of intrusion detection in the network management. Here, the classification effectiveness of the DCNN is increased by using the BAT optimization strategy. The suggested framework combines a DCNN model, which is excellent in pattern data collection, pre-processing by using normalization, and prediction tasks, with the BAT Optimized with Deep Convolutional Neural Network (BATO-DCNN) method, recognized for its capacity to identify optimum solutions in the challenging search spaces. The suggested method effectively tunes the DCNN using BAT optimization, leading to the better convergence and accuracy. The results of the research show that the recommended methodology performs better than traditional approaches in terms of accuracy, precision, F1-score, and recall measures. The results of this study support the current research in the area of network security and open the door for improved network management systems.

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ADT: AI-Driven network Telemetry processing on routers

Cited by 2 publications

References 33 publications

Efficient end-to-end failure probing matrix construction in data center networks

Efficient end-to-end failure probing matrix construction in data center networks

Revolutionizing network management with an AI-driven intrusion detection system

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