Novel approaches in modelling dynamics of networked surveillance environment

Dickinson, Peter; Kraetzl, M.

doi:10.1109/icif.2003.177461

Cited by 7 publications

(10 citation statements)

References 26 publications

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“…Objects such as vertices or edges (or their combinations) associated with a graph are referred to as elements of the graph [5,8]. A weight is a function whose domain is a set of graph elements in G. The domain can be restricted to that of edge or vertex elements only, where the function is referred to as 'edge weight' or 'vertex weight', respectively.…”

Section: The Network As a Graphmentioning

confidence: 99%

“…Computer network communications at the logical network layer and above are represented as graphs [5,8]. A single graph G represents the logical communications of a computer network over an arbitrary observation time interval.…”

Section: The Network As a Graphmentioning

confidence: 99%

“…A cost function associates a cost with each edit operation. Simple ged cost functions have been successfully employed for the general case of network change detection [8,16]. This study explores new ged cost functions, to facilitate the detection of specific traffic related anomalies, which would otherwise be overlooked by the simple cost functions.…”

Section: Introductionmentioning

confidence: 99%

“…A novel approach, to complement the aforementioned techniques, is to treat a periodically observed network as a time series of graphs, and apply graph matching techniques to monitor and detect anomalous changes in the network [5,7,8,16]. In this approach a network observation is represented by a labelled and directed graph.…”

Section: Introductionmentioning

confidence: 99%

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Investigation of graph edit distance cost functions for detection of network anomalies

Kapsabelis¹,

Dickinson²,

Doğançay³

2007

ANZIAMJ

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Computer networks are becoming ubiquitous. Accurately monitoring and managing the behaviour of these complex and dynamic networks is a challenging task. It has become crucial to develop and employ good network monitoring techniques that assist in identifying and correcting abnormalities that affect network reliability, performance, security and future planning. There has been significant research in the detection of change and anomalous events in computer networks. A recent novel approach represents the logical communications of a periodically observed network as a time series of graphs and applies the graph matching technique, graph edit distance, to monitor and detect anomalous behaviour in the network. To date, only simple cost functions for graph edit operations have been used in application to computer network monitoring. This article investigates simple See

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Section: The Network As a Graphmentioning

confidence: 99%

Section: The Network As a Graphmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Investigation of graph edit distance cost functions for detection of network anomalies

Kapsabelis¹,

Dickinson²,

Doğançay³

2007

ANZIAMJ

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show abstract

“…(1) Graph Similarity based Outlier Detection Algorithms: Various graph distance metrics can be used to create time series of network changes by sequentially comparing graphs from adjacent periods. These time series are individually modeled as univariate autoregressive moving average (ARMA) processes and then outliers can be detected [5,18]. (2) Evolutionary Community Outlier Detection Algorithms: Gupta et al [9,10] study an interplay of community detection and temporal outlier detection to discover evolutionary community outliers and community trend outliers.…”

Section: Related Workmentioning

confidence: 99%

Local Learning for Mining Outlier Subgraphs from Network Datasets

Gupta

Mallya

Roy

et al. 2014

Proceedings of the 2014 SIAM International Conference on Data Mining

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In the real world, various systems can be modeled using entity-relationship graphs. Given such a graph, one may be interested in identifying suspicious or anomalous subgraphs. Specifically, a user may want to identify suspicious subgraphs matching a query template. A subgraph can be defined as anomalous based on the connectivity structure within itself as well as with its neighborhood. For example for a co-authorship network, given a subgraph containing three authors, one expects all three authors to be say data mining authors. Also, one expects the neighborhood to mostly consist of data mining authors. But a 3-author clique of data mining authors with all theory authors in the neighborhood clearly seems interesting. Similarly, having one of the authors in the clique as a theory author when all other authors (both in the clique and neighborhood) are data mining authors, is also suspicious. Thus, existence of lowprobability links and absence of high-probability links can be a good indicator of subgraph outlierness. The probability of an edge can in turn be modeled based on the weighted similarity between the attribute values of the nodes linked by the edge. We claim that the attribute weights must be learned locally for accurate link existence probability computations. In this paper, we design a system that finds subgraph outliers given a graph and a query by modeling the problem as a linear optimization. Experimental results on several synthetic and real datasets show the effectiveness of the proposed approach in computing interesting outliers.

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Outlier Detection for Temporal Data

Gupta

Aggarwal²

2014

Synthesis Lectures on Data Mining and Knowledge Discovery

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Novel approaches in modelling dynamics of networked surveillance environment

Cited by 7 publications

References 26 publications

Investigation of graph edit distance cost functions for detection of network anomalies

Investigation of graph edit distance cost functions for detection of network anomalies

Local Learning for Mining Outlier Subgraphs from Network Datasets

Outlier Detection for Temporal Data

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