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  1. Proceedings of the Seventh International Symposium on Visualization for Cyber Security (VizSec '10)
  2. Traffic classification using visual motifs: an empirical evaluation
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EMBER: a global perspective on extreme malicious behavior
Proposing a multi-touch interface for intrusion detection environments
Visualizing attack graphs, reachability, and trust relationships with NAVIGATOR
Visualizing graph dynamics and similarity for enterprise network security and management
Visual analysis of code security
PeekKernelFlows: peeking into IP flows
Visualizing host traffic through graphs
Visualizing your key for secure phone calls and language independence
Traffic classification using visual motifs: an empirical evaluation
Real-time visualization of network behaviors for situational awareness
Interactive detection of network anomalies via coordinated multiple views
Nimble cybersecurity incident management through visualization and defensible recommendations

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Abstract using visual motifs to classify encrypted traffic.

Traffic classification using visual motifs: an empirical evaluation

Content Provider ACM Digital Library
Author Lian, Wilson McHugh, John Monrose, Fabian
Abstract In this paper, we explore the effectiveness of using graphical methods for isolating the differences between common application protocols---both in their transient and steady-state behavior. Specifically, we take advantage of the observation that many Internet application protocols proscribe a very specific series of client/server interactions that are clearly visible in the sizes and timing of packets produced at the network layer and below. We show how so-called "visual motifs" built on these features can be used to assist a human operator to recognize application protocols in unidentified traffic. From a practical point of view, visual traffic classification can be used, for example, for anomaly detection to verify that all traffic to a web server on TCP port 80 does indeed exhibit the characteristic behavior patterns of HTTP, or for misuse detection to find unauthorized servers or to identify traffic generated by prohibited applications. We present our technique for building a classifier based on the notion of visual motifs and report on our experience using this technique to automatically classify on-the-wire behavioral patterns from network flow data collected from a campus network. Specifically, we analyze over 1 billion flows corresponding to over 5 million sessions on nearly 200 distinct ports and show that our approach achieves high recall and precision.
Starting Page 70
Ending Page 78
Page Count 9
File Format PDF
ISBN 9781450300131
DOI 10.1145/1850795.1850804
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2010-09-14
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Evaluation Information visualization Traffic analysis Network management Security Traffic visualization
Content Type Text
Resource Type Article
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