arxiv_ml 50% Match Research Paper Network security analysts,Cybersecurity researchers,Network engineers 20 hours ago

Network Anomaly Traffic Detection via Multi-view Feature Fusion

graph-neural-networks › graph-learning

📄 Abstract

Abstract: Traditional anomalous traffic detection methods are based on single-view analysis, which has obvious limitations in dealing with complex attacks and encrypted communications. In this regard, we propose a Multi-view Feature Fusion (MuFF) method for network anomaly traffic detection. MuFF models the temporal and interactive relationships of packets in network traffic based on the temporal and interactive viewpoints respectively. It learns temporal and interactive features. These features are then fused from different perspectives for anomaly traffic detection. Extensive experiments on six real traffic datasets show that MuFF has excellent performance in network anomalous traffic detection, which makes up for the shortcomings of detection under a single perspective.

Key Contributions

Proposes Multi-view Feature Fusion (MuFF) for network anomaly traffic detection, addressing limitations of single-view analysis. MuFF models temporal and interactive packet relationships to learn and fuse features from different perspectives, improving detection of complex attacks and encrypted communications.

Business Value

Enhances network security by providing more robust and accurate detection of anomalous traffic, which can prevent data breaches, service disruptions, and financial losses for organizations.

Paper Metadata

Innovation Type

Algorithmic

Deployment Feasibility

Potentially feasible, depending on computational resources and integration with existing network monitoring tools. Real-time performance would be a key factor.

Limitations Addressed

Limitations of traditional single-view analysis in detecting complex attacks and encrypted communications.

Technical Tags

network anomaly detectionfeature fusiontemporal analysispacket analysisencrypted trafficmulti-view learningdeep learningtraffic modeling

Research Topics

Network SecurityMachine LearningData AnalysisCybersecurityAnomaly Detection

Methods & Architectures

Multi-view Feature Fusion (MuFF)temporal feature learninginteractive feature learning

Applications & Tasks

Network Security Cybersecurity Anomaly DetectionTraffic Analysis Network Anomaly Traffic Detection

Datasets & Benchmarks

Datasets

six real traffic datasets

Related Fields

Computer NetworksCybersecurityMachine LearningData Mining

Keywords

network anomaly detectiontraffic analysisfeature fusionmulti-view learningdeep learningcybersecuritypacket analysistemporal relationshipsinteractive relationshipsencrypted trafficnetwork securityanomaly detection systems

Academic Context

#Network Security#Machine Learning#Data Analysis#Cybersecurity#Anomaly Detection

Commercial Potential

Potential Products

Advanced Intrusion Detection SystemsNetwork Traffic Monitoring Software

Target Industries

TelecommunicationsFinanceHealthcareGovernment

Use Case Examples

Detecting sophisticated cyberattacksIdentifying malicious encrypted trafficMonitoring network health and performance

Competitive Edge

Offers improved performance over single-view methods by leveraging multi-view feature fusion, potentially outperforming existing anomaly detection techniques that rely on limited perspectives.

Resource Requirements

Compute Needs

Likely requires significant computational resources for training deep learning models on large traffic datasets.

Data Requirements

Requires large, labeled or unlabeled network traffic datasets with diverse attack scenarios.

Deployment Constraints

Real-time processing capabilities, integration with existing network infrastructure, and potential for false positives/negatives.

Scalability

Scalability depends on the efficiency of the feature fusion and detection mechanisms, and the underlying infrastructure.

Production Readiness

Maturity Level

Research

View Full Paper Back to Papers