Visual Analysis of Network Traffic for Resource Planning, Interactive Monitoring, and Interpretation of Security Threats

Mansmann, Florian; Keim, Daniel A.; North, Stephen C.; Rexroad, Brian; Sheleheda, D.

doi:10.1109/tvcg.2007.70522

Cited by 61 publications

(48 citation statements)

References 17 publications

(21 reference statements)

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“…For spatial layout of data with a geographic component, measures of distance and angular displacement can be used to assess the degree to which the treemap reflects the spatial properties of the data it represents. This has allowed us to make comparisons between our spatial layout and the HistoMap layout [17], identifying the types of spatial pattern that are best represented by each layout. Yet summary statistics of overall spatial distortion or consistency fail to detect the impact of discontinuities in layout.…”

Section: Resultsmentioning

confidence: 99%

“…We propose here a new spatial layout that attempts to position each node as closely as possible to its geographic location while minimizing its aspect ratio. The HistoMap layout of Mansmann et al [17] uses a variation of the pivot layout [23,2] to place nodes according to their position relative to the pivot in their parent node. Here we propose an alternative strategy that is a refinement of the orderedSquarified layout.…”

Section: Spatial Layout and Displacementmentioning

confidence: 99%

“…'Simulation' represents the mean of 1000 realizations of 100 log-normal randomly sized nodes with Gaussian locations; 'France' represents the 95 departements shown in Figure 6; 'OSGB' represents the Ordnance Survey National Grid squares shown in Figure 8; 'US Population' represents the US states sized according to population shown in Figure 9. compare the effect of minor changes to the spatial layout algorithm as well as comparison with the geographic HistoMap layout of Mansmann et al [17]. The results for the spatial layout and the HistoMap for simulated and real geographic datasets are shown in Table 3.…”

Section: Numeric Measuresmentioning

confidence: 99%

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Spatially Ordered Treemaps

Wood

Dykes

2008

IEEE Trans. Visual. Comput. Graphics

133

128

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This is the unspecified version of the paper.This version of the publication may differ from the final published version. Abstract-Existing treemap layout algorithms suffer to some extent from poor or inconsistent mappings between data order and visual ordering in their representation, reducing their cognitive plausibility. While attempts have been made to quantify this mismatch, and algorithms proposed to minimize inconsistency, solutions provided tend to concentrate on one-dimensional ordering. We propose extensions to the existing squarified layout algorithm that exploit the two-dimensional arrangement of treemap nodes more effectively. Our proposed spatial squarified layout algorithm provides a more consistent arrangement of nodes while maintaining low aspect ratios. It is suitable for the arrangement of data with a geographic component and can be used to create tessellated cartograms for geovisualization. Locational consistency is measured and visualized and a number of layout algorithms are compared. CIELab color space and displacement vector overlays are used to assess and emphasize the spatial layout of treemap nodes. A case study involving locations of tagged photographs in the Flickr database is described. Permanent repository link

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Section: Resultsmentioning

confidence: 99%

Section: Spatial Layout and Displacementmentioning

confidence: 99%

Section: Numeric Measuresmentioning

confidence: 99%

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Spatially Ordered Treemaps

Wood

Dykes

2008

IEEE Trans. Visual. Comput. Graphics

133

128

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“…in addition to the spatial coordinates, while others use traditional representation techniques such as histograms [25,47,48], histographs [39] or other graphs [29,32]. The novel IDS proposed in this work employs the glyph metaphor as well, using different colours and shapes in addition to the spatial coordinates to offer information about the protocol each packet belongs to.…”

Section: Previous Workmentioning

confidence: 99%

Neural visualization of network traffic data for intrusion detection

Corchado

Herrero

2011

Applied Soft Computing

149

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-This study introduces and describes a novel Intrusion Detection System (IDS) called MOVCIDS (MObile Visualization Connectionist IDS). This system applies neural projection architectures to detect anomalous situations taking place in a computer network. By its advanced visualization facilities, the proposed IDS allows providing an overview of the network traffic as well as identifying anomalous situations tackled by computer networks, responding to the challenges presented by volume, dynamics and diversity of the traffic, including novel (0-day) attacks. MOVCIDS provides a novel point of view in the field of IDSs by enabling the most interesting projections (based on the fourth order statistics; the kurtosis index) of a massive traffic dataset to be extracted. These projections are then depicted through a functional and mobile visualization interface, providing visual information of the internal structure of the traffic data. The interface makes MOVCIDS accessible from any mobile device to give more accessibility to network administrators, enabling continuous visualization, monitoring and supervision of computer networks. Additionally, a novel testing technique has been developed to evaluate MOVCIDS and other IDSs employing numerical datasets. To show the performance and validate the proposed IDS, it has been tested in different real domains containing several attacks and anomalous situations. In addition, the importance of the temporal dimension on intrusion detection, and the ability of this IDS to process it, are emphasized in this work.

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“…Hierarchical Network Maps [16] are one example of how visualization can facilitate the interpretation of network capacities. In particular, this technique uses a hierarchy of continents, countries, autonomous systems and IP prefixes to render a TreeMap [10] of the internet.…”

Section: Infrastructure Planning and Testingmentioning

confidence: 99%

Visual Analytics: Towards Intelligent Interactive Internet and Security Solutions

Davey

Mansmann

Kohlhammer

et al. 2012

The Future Internet

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Abstract. In the Future Internet, Big Data can not only be found in the amount of traffic, logs or alerts of the network infrastructure, but also on the content side. While the term big data refers to the increase of the available data, this implicitly means that we must deal with problems at larger scale and thus hints at scalability issues in the analysis process of such data sets. Visual Analytics is an enabling technology, that offers new ways of extracting information from big data through intelligent, interactive internet and security solutions. It derives its effectiveness both from scalable analysis algorithms, that allow processing of large data sets, and from scalable visualizations. These visualizations take advantage of human background knowledge and pattern detection capabilities to find yet unknown patterns, to detect trends and to relate these findings to a holistic view on the problems. Besides discussing the origins of Visual Analytics, this paper presents concrete examples of how the two facets of content and infrastructure of the Future Internet can benefit from Visual Analytics. In conclusion, it is the confluence of both technologies that will open up new opportunities for businesses, e-governance and the public.

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Visual Analysis of Network Traffic for Resource Planning, Interactive Monitoring, and Interpretation of Security Threats

Cited by 61 publications

References 17 publications

Spatially Ordered Treemaps

Spatially Ordered Treemaps

Neural visualization of network traffic data for intrusion detection

Visual Analytics: Towards Intelligent Interactive Internet and Security Solutions

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