  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 the model
dynamically and ensure sustained performance over time. Our approach
significantly reduces the time required for failure detection. The initial
training phase uses a Voice Over LTE (VoLTE) dataset, demonstrating the model's
effectiveness and generalizability when using transfer learning on Mobility
Management services. This work highlights the potential of integrating computer
vision techniques in network analysis, offering a scalable and efficient
solution for real-time network failure detection.
