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  1. Proceedings of the Seventh International Symposium on Visualization for Cyber Security (VizSec '10)
  2. Interactive detection of network anomalies via coordinated multiple views
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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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Interactive detection of network anomalies via coordinated multiple views

Content Provider ACM Digital Library
Author Lu, Aidong Harrison, Lane Wang, Weichao Hu, Xianlin Ying, Xiaowei Wu, Xintao
Abstract This paper presents a new approach to intrusion detection that supports the identification and analysis of network anomalies using an interactive coordinated multiple views (CMV) mechanism. A CMV visualization consisting of a node-link diagram, scatterplot, and time histogram is described that allows interactive analysis from different perspectives, as some network anomalies can only be identified through joint features in the provided spaces. Spectral analysis methods are integrated to provide visual cues that allow identification of malicious nodes. An adjacency-based method is developed to generate the time histogram, which allows users to select time ranges in which suspicious activity occurs. Data from Sybil attacks in simulated wireless networks is used as the test bed for the system. The results and discussions demonstrate that intrusion detection can be achieved with a few iterations of CMV exploration. Quantitative results are collected on the accuracy of our approach and comparisons are made to single domain exploration and other high-dimensional projection methods. We believe that this approach can be extended to anomaly detection in general networks, particularly to Internet networks and social networks.
Starting Page 91
Ending Page 101
Page Count 11
File Format PDF
ISBN 9781450300131
DOI 10.1145/1850795.1850806
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2010-09-14
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Coordinated multiple views Intrusion detection Sybil attacks Security visualization Spectral analysis
Content Type Text
Resource Type Article
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