Charles Abondo

Lukasz Tulczyjew, Kinan Jarrah, Charles Abondo et al.

The integration of advanced technologies into telecommunication networks complicates troubleshooting, posing challenges for manual error identification in Packet Capture (PCAP) data. This manual approach, requiring substantial resources, becomes impractical at larger scales. Machine learning (ML) methods offer alternatives, but the scarcity of labeled data limits accuracy. In this study, we propose a self-supervised, large language model-based (LLMcap) method for PCAP failure detection. LLMcap leverages language-learning abilities and employs masked language modeling to learn grammar, context, and structure. Tested rigorously on various PCAPs, it demonstrates high accuracy despite the absence of labeled data during training, presenting a promising solution for efficient network analysis. Index Terms: Network troubleshooting, Packet Capture Analysis, Self-Supervised Learning, Large Language Model, Network Quality of Service, Network Performance.

Lukasz Tulczyjew, Ihor Biruk, Murat Bilgic et al.

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.