LLMcap: Large Language Model for Unsupervised PCAP Failure Detection

• URL: http://arxiv.org/abs/2407.06085v1
• Date: Wed, 3 Jul 2024 09:59:27 GMT
• Title: LLMcap: Large Language Model for Unsupervised PCAP Failure Detection
• Abstract summary: Machine learning methods offer alternatives, but the scarcity of labeled data limits accuracy. We propose a self-supervised, large language model-based (LLMcap) method for PCAP failure detection.
• Score: 1.0485739694839669
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• How LLMs Fail and Generalize in RTL Coding for Hardware Design? [56.361436215029045]
  We introduce a new error taxonomy grounded in problem solvability, inspired by cognitive theory.<n>Our taxonomy categorizes failures into syntactic, semantic, solvable functional, and unsolvable functional types.
  arXiv  Detail & Related papers  (2026-04-26T14:34:49Z)
• A Survey of Machine Learning-based Physical-Layer Authentication in Wireless Communications [17.707450193500698]
  Physical-Layer Authentication (PLA) is emerging as a promising complement due to its exploitation of unique properties in wireless environments. This paper presents a comprehensive survey of characteristics and technologies that can be used in the ML-based PLA.
  arXiv  Detail & Related papers  (2024-11-15T03:01:23Z)
• Enhancing Multi-Step Reasoning Abilities of Language Models through Direct Q-Function Optimization [50.485788083202124]
  Reinforcement Learning (RL) plays a crucial role in aligning large language models with human preferences and improving their ability to perform complex tasks. We introduce Direct Q-function Optimization (DQO), which formulates the response generation process as a Markov Decision Process (MDP) and utilizes the soft actor-critic (SAC) framework to optimize a Q-function directly parameterized by the language model. Experimental results on two math problem-solving datasets, GSM8K and MATH, demonstrate that DQO outperforms previous methods, establishing it as a promising offline reinforcement learning approach for aligning language models.
  arXiv  Detail & Related papers  (2024-10-11T23:29:20Z)
• CodeUnlearn: Amortized Zero-Shot Machine Unlearning in Language Models Using Discrete Concept [5.345828824625758]
  We propose a novel amortized unlearning approach using codebook features and Sparse Autoencoders (SAEs) By leveraging a bottleneck to decompose the activation space and regulate information flow, our method efficiently unlearns targeted information while preserving the model's performance on unrelated data.
  arXiv  Detail & Related papers  (2024-10-08T10:26:22Z)
• Semantic Meta-Split Learning: A TinyML Scheme for Few-Shot Wireless Image Classification [50.28867343337997]
  This work presents a TinyML-based semantic communication framework for few-shot wireless image classification. We exploit split-learning to limit the computations performed by the end-users while ensuring privacy-preserving. meta-learning overcomes data availability concerns and speeds up training by utilizing similarly trained tasks.
  arXiv  Detail & Related papers  (2024-09-03T05:56:55Z)
• R-SFLLM: Jamming Resilient Framework for Split Federated Learning with Large Language Models [65.04475956174959]
  Split federated learning (SFL) is a compute-efficient paradigm in distributed machine learning (ML)<n>A significant challenge in SFL, particularly when deployed over wireless channels, is the susceptibility of transmitted model parameters to adversarial jamming.<n>This paper develops a physical layer framework for resilient SFL with large language models (LLMs) and vision language models (VLMs) over wireless networks.
  arXiv  Detail & Related papers  (2024-07-16T12:21:29Z)
• PCAPVision: PCAP-Based High-Velocity and Large-Volume Network Failure Detection [1.0485739694839669]
  We present PCAPVision, a novel approach that utilizes computer vision and Convolutional Neural Networks (CNNs) to detect failures in PCAP files. Our approach significantly reduces the time required for failure detection.
  arXiv  Detail & Related papers  (2024-07-01T13:52:17Z)
• Transfer Learning in Pre-Trained Large Language Models for Malware Detection Based on System Calls [3.5698678013121334]
  This work presents a novel framework leveraging large language models (LLMs) to classify malware based on system call data. Experiments with a dataset of over 1TB of system calls demonstrate that models with larger context sizes, such as BigBird and Longformer, achieve superior accuracy and F1-Score of approximately 0.86. This approach shows significant potential for real-time detection in high-stakes environments, offering a robust solution to evolving cyber threats.
  arXiv  Detail & Related papers  (2024-05-15T13:19:43Z)
• HuntGPT: Integrating Machine Learning-Based Anomaly Detection and Explainable AI with Large Language Models (LLMs) [0.09208007322096533]
  We present HuntGPT, a specialized intrusion detection dashboard applying a Random Forest classifier. The paper delves into the system's architecture, components, and technical accuracy, assessed through Certified Information Security Manager (CISM) Practice Exams. The results demonstrate that conversational agents, supported by LLM and integrated with XAI, provide robust, explainable, and actionable AI solutions in intrusion detection.
  arXiv  Detail & Related papers  (2023-09-27T20:58:13Z)
• The Devil is in the Errors: Leveraging Large Language Models for Fine-grained Machine Translation Evaluation [93.01964988474755]
  AutoMQM is a prompting technique which asks large language models to identify and categorize errors in translations. We study the impact of labeled data through in-context learning and finetuning. We then evaluate AutoMQM with PaLM-2 models, and we find that it improves performance compared to just prompting for scores.
  arXiv  Detail & Related papers  (2023-08-14T17:17:21Z)
• Discover, Explanation, Improvement: An Automatic Slice Detection Framework for Natural Language Processing [72.14557106085284]
  slice detection models (SDM) automatically identify underperforming groups of datapoints. This paper proposes a benchmark named "Discover, Explain, improve (DEIM)" for classification NLP tasks. Our evaluation shows that Edisa can accurately select error-prone datapoints with informative semantic features.
  arXiv  Detail & Related papers  (2022-11-08T19:00:00Z)
• DATA: Domain-Aware and Task-Aware Pre-training [94.62676913928831]
  We present DATA, a simple yet effective NAS approach specialized for self-supervised learning (SSL) Our method achieves promising results across a wide range of computation costs on downstream tasks, including image classification, object detection and semantic segmentation.
  arXiv  Detail & Related papers  (2022-03-17T02:38:49Z)
• Improving Classifier Training Efficiency for Automatic Cyberbullying Detection with Feature Density [58.64907136562178]
  We study the effectiveness of Feature Density (FD) using different linguistically-backed feature preprocessing methods. We hypothesise that estimating dataset complexity allows for the reduction of the number of required experiments. The difference in linguistic complexity of datasets allows us to additionally discuss the efficacy of linguistically-backed word preprocessing.
  arXiv  Detail & Related papers  (2021-11-02T15:48:28Z)
• Prototypical Contrastive Learning of Unsupervised Representations [171.3046900127166]
  Prototypical Contrastive Learning (PCL) is an unsupervised representation learning method. PCL implicitly encodes semantic structures of the data into the learned embedding space. PCL outperforms state-of-the-art instance-wise contrastive learning methods on multiple benchmarks.
  arXiv  Detail & Related papers  (2020-05-11T09:53:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.