Papers Traffic Classification
“Traffic Classification” 태그가 달린 논문 110편 · 필터 해제
Energy-Efficient Deep Learning for Traffic Classification on Microcontrollers
In this paper, we present a practical deep learning (DL) approach for energy-efficient traffic classification (TC) on resource-limited microcontrollers, which are widely used in IoT-based smart systems and communication …
Computational EfficiencyDeep LearningHardware Aware Neural Architecture SearchNeural Architecture Search+1Efficient Traffic Classification using HW-NAS: Advanced Analysis and Optimization for Cybersecurity on Resource-Constrained Devices
This paper presents a hardware-efficient deep neural network (DNN), optimized through hardware-aware neural architecture search (HW-NAS); the DNN supports the classification of session-level encrypted traffic on resource…
Hardware Aware Neural Architecture SearchNeural Architecture SearchTraffic ClassificationWhen Simple Model Just Works: Is Network Traffic Classification in Crisis?
Machine learning has been applied to network traffic classification (TC) for over two decades. While early efforts used shallow models, the latter 2010s saw a shift toward complex neural networks, often reporting near-pe…
Traffic ClassificationRespond to Change with Constancy: Instruction-tuning with LLM for Non-I.I.D. Network Traffic Classification
Encrypted traffic classification is highly challenging in network security due to the need for extracting robust features from content-agnostic traffic data. Existing approaches face critical issues: (i) Distribution dri…
Robust classificationTraffic ClassificationIntelligent Detection of Non-Essential IoT Traffic on the Home Gateway
The rapid expansion of Internet of Things (IoT) devices, particularly in smart home environments, has introduced considerable security and privacy concerns due to their persistent connectivity and interaction with cloud …
Binary ClassificationTraffic ClassificationFastFlow: Early Yet Robust Network Flow Classification using the Minimal Number of Time-Series Packets
Network traffic classification is of great importance for network operators in their daily routines, such as analyzing the usage patterns of multimedia applications and optimizing network configurations. Internet service…
ClassificationTime SeriesTraffic ClassificationHierarchical Local-Global Feature Learning for Few-shot Malicious Traffic Detection
With the rapid growth of internet traffic, malicious network attacks have become increasingly frequent and sophisticated, posing significant threats to global cybersecurity. Traditional detection methods, including rule-…
Few-Shot LearningTraffic ClassificationLarge Language Models powered Malicious Traffic Detection: Architecture, Opportunities and Case Study
Malicious traffic detection is a pivotal technology for network security to identify abnormal network traffic and detect network attacks. Large Language Models (LLMs) are trained on a vast corpus of text, have amassed re…
Traffic ClassificationPacketCLIP: Multi-Modal Embedding of Network Traffic and Language for Cybersecurity Reasoning
Traffic classification is vital for cybersecurity, yet encrypted traffic poses significant challenges. We present PacketCLIP, a multi-modal framework combining packet data with natural language semantics through contrast…
Anomaly DetectionClassificationGraph Neural NetworkIntrusion Detection+2Network Traffic Classification Using Machine Learning, Transformer, and Large Language Models
This study uses various models to address network traffic classification, categorizing traffic into web, browsing, IPSec, backup, and email. We collected a comprehensive dataset from Arbor Edge Defender (AED) devices, co…
Few-Shot LearningModel SelectionTraffic ClassificationUniversal Embedding Function for Traffic Classification via QUIC Domain Recognition Pretraining: A Transfer Learning Success
Encrypted traffic classification (TC) methods must adapt to new protocols and extensions as well as to advancements in other machine learning fields. In this paper, we follow a transfer learning setup best known from com…
Traffic ClassificationTransfer LearningRevolutionizing Encrypted Traffic Classification with MH-Net: A Multi-View Heterogeneous Graph Model
With the growing significance of network security, the classification of encrypted traffic has emerged as an urgent challenge. Traditional byte-based traffic analysis methods are constrained by the rigid granularity of i…
Contrastive LearningTraffic ClassificationNetFlowGen: Leveraging Generative Pre-training for Network Traffic Dynamics
Understanding the traffic dynamics in networks is a core capability for automated systems to monitor and analyze networking behaviors, reducing expensive human efforts and economic risks through tasks such as traffic cla…
Self-Supervised LearningTraffic ClassificationImproving the network traffic classification using the Packet Vision approach
The network traffic classification allows improving the management, and the network services offer taking into account the kind of application. The future network architectures, mainly mobile networks, foresee intelligen…
Traffic ClassificationVINEVI: A Virtualized Network Vision Architecture for Smart Monitoring of Heterogeneous Applications and Infrastructures
Monitoring heterogeneous infrastructures and applications is essential to cope with user requirements properly, but it still lacks enhancements. The well-known state-of-the-art methods and tools do not support seamless m…
ClassificationTraffic ClassificationMIETT: Multi-Instance Encrypted Traffic Transformer for Encrypted Traffic Classification
Network traffic includes data transmitted across a network, such as web browsing and file transfers, and is organized into packets (small units of data) and flows (sequences of packets exchanged between two endpoints). C…
Contrastive LearningTraffic ClassificationMERLOT: A Distilled LLM-based Mixture-of-Experts Framework for Scalable Encrypted Traffic Classification
We present MERLOT, a scalable mixture-of-expert (MoE) based refinement of distilled large language model optimized for encrypted traffic classification. By applying model distillation techniques in a teacher-student para…
DecoderLanguage ModelingLanguage ModellingLarge Language Model+2Discern-XR: An Online Classifier for Metaverse Network Traffic
In this paper, we design an exclusive Metaverse network traffic classifier, named Discern-XR, to help Internet service providers (ISP) and router manufacturers enhance the quality of Metaverse services. Leveraging segmen…
Mixed RealityTraffic ClassificationGroup & Reweight: A Novel Cost-Sensitive Approach to Mitigating Class Imbalance in Network Traffic Classification
Internet services have led to the eruption of network traffic, and machine learning on these Internet data has become an indispensable tool, especially when the application is risk-sensitive. This paper focuses on networ…
Traffic ClassificationPacket Inspection Transformer: A Self-Supervised Journey to Unseen Malware Detection with Few Samples
As networks continue to expand and become more interconnected, the need for novel malware detection methods becomes more pronounced. Traditional security measures are increasingly inadequate against the sophistication of…
Few-Shot LearningMalware DetectionSelf-Supervised LearningTraffic Classification