PCAPVision: PCAP-Based High-Velocity and Large-Volume Network Failure Detection
Lukasz Tulczyjew, Ihor Biruk, Murat Bilgic, Charles Abondo, Nathanael, Weill

TL;DR
PCAPVision introduces a computer vision-based CNN approach that converts network traffic data into images for rapid, accurate, and scalable failure detection in large-scale networks, with an adaptive continual learning framework.
Contribution
This paper presents a novel method that transforms PCAP data into images for CNN-based failure detection, incorporating continual learning for dynamic adaptation.
Findings
Effective failure detection on VoLTE datasets
Significant reduction in detection time
Model generalizes well with transfer learning
Abstract
Detecting failures via analysis of Packet Capture (PCAP) files is crucial for maintaining network reliability and performance, especially in large-scale telecommunications networks. Traditional methods, relying on manual inspection and rule-based systems, are often too slow and labor-intensive to meet the demands of modern networks. In this paper, we present PCAPVision, a novel approach that utilizes computer vision and Convolutional Neural Networks (CNNs) to detect failures in PCAP files. By converting PCAP data into images, our method leverages the robust pattern recognition capabilities of CNNs to analyze network traffic efficiently. This transformation process involves encoding packet data into structured images, enabling rapid and accurate failure detection. Additionally, we incorporate a continual learning framework, leveraging automated annotation for the feedback loop, to adapt…
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Taxonomy
TopicsSoftware System Performance and Reliability · Anomaly Detection Techniques and Applications · Network Security and Intrusion Detection
