YMTIS Workshop Geneva Introduction to XNA Marios Karagiannis
Exploring Graph-based Network Traffic Analysisalumni.cs.ucr.edu/~marios/Papers/poster.pdfTraffic...
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Exploring Graph-based Network Traffic Analysis Marios Iliofotou, H. Kim, M. Faloutsos, M. Mitzenmacher, P. Pappu, and G. Varghese Department of Computer Science and Engineering - University of California, Riverside Traffic Analysis Using Traffic Dispersion Graphs (TDGs) What is this poster about 1. How to generate and model TDGs 2. Practical applications of TDGs Graph formation (toy example) Example of a P2P TDG: Gnutella - One large connected component - High average degree [Trace from: Palo Alto Internet Exchange.] Example of a Client- Server TDG: HTTP - Large number of small components - Graph is less dense compared to P2P TDGs [Trace from: Palo Alto Internet Exchange.] Graph Classification - Small set of metrics can Graption: Graph-based Traffic Classification Edge Selection • Using well-known ports and payload • Using machine learning classification and clustering methods - Small set of metrics can distinguish between application classes - Scatter plot shows that fixed threshold can classify TDGs across multiple backbone locations (4 traces) 75 80 85 90 95 100 Precision Recall Graption BLINC 50 55 60 65 70 75 80 85 90 95 100 Precision Recall About Graption • Classifies network flows using network-wide interactions • Case study: Use Graption to detect P2P Traffic • Graption can automate the extraction of application profile • Performs better than host-based methods at the backbone The Graption Methodology Graption over various system configurations Per application classification performance Classification Performance (%) Classification Performance (%) F-Measure (%) Number of Clusters
Transcript of Exploring Graph-based Network Traffic Analysisalumni.cs.ucr.edu/~marios/Papers/poster.pdfTraffic...
Exploring Graph-based Network Traffic Analysis
Marios Iliofotou, H. Kim, M. Faloutsos, M. Mitzenmacher, P. Pappu, and G. Varghese
Department of Computer Science and Engineering - University of California, Riverside
Traffic Analysis Using Traffic Dispersion Graphs (TDGs)
� What is this poster about1. How to generate and model TDGs2. Practical applications of TDGs
� Graph formation (toy example)
Example of a P2P TDG:Gnutella- One large connected component
- High average degree
[Trace from: Palo Alto Internet Exchange.]
Example of a Client-Server TDG: HTTP- Large number of small components
- Graph is less dense
compared to P2P TDGs[Trace from: Palo Alto Internet Exchange.]
Graph Classification- Small set of metrics can
Graption: Graph-based Traffic Classification
� Edge Selection• Using well-known ports and payload • Using machine learning classification and clustering methods
- Small set of metrics can distinguish between application classes
- Scatter plot shows that fixed
threshold can classify TDGsacross multiple backbone locations (4 traces)
75
80
85
90
95
100
Precision Recall
Graption
BLINC
50556065707580859095
100
Precision Recall
� About Graption• Classifies network flows using network-wide interactions
• Case study: Use Graptionto detect P2P Traffic
• Graption can automate the extraction of application profile
• Performs better than host-based methodsat the backbone
The Graption MethodologyGraption over varioussystem configurations
Per application classificationperformance
Cla
ssif
icati
on
Perfo
rm
ance (
%)
Cla
ssif
icati
on P
erfo
rm
ance (
%)
F-M
easure (%
)
Number of Clusters
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