Papers Traffic Classification
“Traffic Classification” 태그가 달린 논문 110편 · 필터 해제
AutoFlow: An Autoencoder-based Approach for IP Flow Record Compression with Minimal Impact on Traffic Classification
Network monitoring generates massive volumes of IP flow records, posing significant challenges for storage and analysis. This paper presents a novel deep learning-based approach to compressing these records using autoenc…
ManagementTraffic ClassificationTowards a graph-based foundation model for network traffic analysis
Foundation models have shown great promise in various fields of study. A potential application of such models is in computer network traffic analysis, where these models can grasp the complexities of network traffic dyna…
Few-Shot LearningIntrusion DetectionLink PredictionTraffic ClassificationTime-Distributed Feature Learning for Internet of Things Network Traffic Classification
Deep learning-based network traffic classification (NTC) techniques, including conventional and class-of-service (CoS) classifiers, are a popular tool that aids in the quality of service (QoS) and radio resource manageme…
Deep LearningTraffic ClassificationTrafficGPT: An LLM Approach for Open-Set Encrypted Traffic Classification
Encrypted traffic has been known to be vulnerable to traffic analysis attacks that exploit the statistical features of encrypted traffic flows, such as packet sizes, timing, and direction, to infer information about the …
Traffic ClassificationEfficiently and Effectively: A Two-stage Approach to Balance Plaintext and Encrypted Text for Traffic Classification
Encrypted traffic classification is the task of identifying the application or service associated with encrypted network traffic. One effective approach for this task is to use deep learning methods to encode the raw tra…
ClassificationTraffic ClassificationTowards a Transformer-Based Pre-trained Model for IoT Traffic Classification
The classification of IoT traffic is important to improve the efficiency and security of IoT-based networks. As the state-of-the-art classification methods are based on Deep Learning, most of the current results require …
ClassificationTraffic ClassificationEnhancing Encrypted Internet Traffic Classification Through Advanced Data Augmentation Techniques
The increasing popularity of online services has made Internet Traffic Classification a critical field of study. However, the rapid development of internet protocols and encryption limits usable data availability. This p…
Data AugmentationTraffic ClassificationDevelopment of Multistage Machine Learning Classifier using Decision Trees and Boosting Algorithms over Darknet Network Traffic
In recent years, the clandestine nature of darknet activities has presented an escalating challenge to cybersecurity efforts, necessitating sophisticated methods for the detection and classification of network traffic as…
Ensemble Learningfeature selectionTraffic ClassificationNon-uniformity is All You Need: Efficient and Timely Encrypted Traffic Classification With ECHO
With 95% of Internet traffic now encrypted, an effective approach to classifying this traffic is crucial for network security and management. This paper introduces ECHO -- a novel optimization process for ML/DL-based enc…
AllClassificationEarly ClassificationHyperparameter Optimization+1NetMamba: Efficient Network Traffic Classification via Pre-training Unidirectional Mamba
Network traffic classification is a crucial research area aiming to enhance service quality, streamline network management, and bolster cybersecurity. To address the growing complexity of transmission encryption techniqu…
ClassificationFew-Shot LearningMambaTraffic ClassificationLarge Language Model (LLM) for Telecommunications: A Comprehensive Survey on Principles, Key Techniques, and Opportunities
Large language models (LLMs) have received considerable attention recently due to their outstanding comprehension and reasoning capabilities, leading to great progress in many fields. The advancement of LLM techniques al…
Language ModelingLanguage ModellingLarge Language ModelPrediction+4Generic Multi-modal Representation Learning for Network Traffic Analysis
Network traffic analysis is fundamental for network management, troubleshooting, and security. Tasks such as traffic classification, anomaly detection, and novelty discovery are fundamental for extracting operational inf…
Anomaly DetectionFeature EngineeringRepresentation LearningTraffic ClassificationFedAuxHMTL: Federated Auxiliary Hard-Parameter Sharing Multi-Task Learning for Network Edge Traffic Classification
Federated Learning (FL) has garnered significant interest recently due to its potential as an effective solution for tackling many challenges in diverse application scenarios, for example, data privacy in network edge tr…
Federated LearningMulti-Task LearningNavigateTraffic ClassificationCBR - Boosting Adaptive Classification By Retrieval of Encrypted Network Traffic with Out-of-distribution
Encrypted network traffic Classification tackles the problem from different approaches and with different goals. One of the common approaches is using Machine learning or Deep Learning-based solutions on a fixed number o…
ClassificationRetrievalTraffic ClassificationOne Train for Two Tasks: An Encrypted Traffic Classification Framework Using Supervised Contrastive Learning
As network security receives widespread attention, encrypted traffic classification has become the current research focus. However, existing methods conduct traffic classification without sufficiently considering the com…
ClassificationContrastive LearningData AugmentationMulti-Task Learning+1Deep Learning Approaches for Network Traffic Classification in the Internet of Things (IoT): A Survey
The Internet of Things (IoT) has witnessed unprecedented growth, resulting in a massive influx of diverse network traffic from interconnected devices. Effectively classifying this network traffic is crucial for optimizin…
ClassificationDeep LearningManagementSurvey+1Data Augmentation for Traffic Classification
Data Augmentation (DA) -- enriching training data by adding synthetic samples -- is a technique widely adopted in Computer Vision (CV) and Natural Language Processing (NLP) tasks to improve models performance. Yet, DA ha…
BenchmarkingClassificationData AugmentationTime Series+1From Classification to Optimization: Slicing and Resource Management with TRACTOR
5G and beyond networks promise advancements in bandwidth, latency, and connectivity. The Open Radio Access Network (O-RAN) framework enhances flexibility through network slicing and closed-loop RAN control. Central to th…
ManagementTraffic ClassificationDataZoo: Streamlining Traffic Classification Experiments
The machine learning communities, such as those around computer vision or natural language processing, have developed numerous supportive tools and benchmark datasets to accelerate the development. In contrast, the netwo…
ClassificationManagementTraffic ClassificationnetFound: Foundation Model for Network Security
Developing generalizable ML-based solutions for disparate learning problems in network security is highly desired. However, despite a rich history of applying ML to network security, most existing solutions lack generali…
Feature Engineeringfeature selectionIntrusion DetectionMissing Labels+4