Anomaly detection in data represented as graphs

Eberle, William; Holder, Lawrence B.

doi:10.3233/ida-2007-11606

Cited by 87 publications

(41 citation statements)

References 12 publications

(11 reference statements)

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“…Eberle and Holder [5,8] were the first to search for a fraudulent pattern that is "similar" (without matching exactly) to frequent, or "good", patterns. Authors note that anyone who is attempting to commit fraud would want their activities to look as real as possible.…”

Section: Related Workmentioning

confidence: 99%

A clique-based method for mining fuzzy graph patterns in anti-money laundering systems

Bershtein

Tselykh

2013

Proceedings of the 6th International Conference on Security of Information and Networks

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In this paper, we present a clique-based method for mining fuzzy graph patterns of money laundering and financing terrorism. The method will contribute to a new generation of intelligent antimoney laundering systems that incorporate comprehensive information from various information sources as well as from human subject matter experts. A fuzzy degree of confidence can therefore be associated to each relation between any two actors in a graph of transactions. We reduce the problem of fuzzy subgraph isomorphism to the problem of finding a fuzzy set of maximal cliques in a fuzzy extension of a mathematical construct that is similar to Vizing's modular product of graphs.

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Section: Related Workmentioning

confidence: 99%

A clique-based method for mining fuzzy graph patterns in anti-money laundering systems

Bershtein

Tselykh

2013

Proceedings of the 6th International Conference on Security of Information and Networks

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“…It is considered as a small deviation from the normal pattern [20]. For example, anyone attempting to peep into someone's social network account would not want to get caught; therefore, he will try to behave in the same manner as a normal user.…”

Section: In-disguise Anomalymentioning

confidence: 99%

A survey of data mining and social network analysis based anomaly detection techniques

Kaur

Singh

2016

Egyptian Informatics Journal

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With the increasing trend of online social networks in different domains, social network analysis has recently become the center of research. Online Social Networks (OSNs) have fetched the interest of researchers for their analysis of usage as well as detection of abnormal activities. Anomalous activities in social networks represent unusual and illegal activities exhibiting different behaviors than others present in the same structure. This paper discusses different types of anomalies and their novel categorization based on various characteristics. A review of number of techniques for preventing and detecting anomalies along with underlying assumptions and reasons for the presence of such anomalies is covered in this paper. The paper presents a review of number of data mining approaches used to detect anomalies. A special reference is made to the analysis of social network centric anomaly detection techniques which are broadly classified as behavior based, structure based and spectral based. Each one of this classification further incorporates number of techniques which are discussed in the paper. The paper has been concluded with different future directions and areas of research that could be addressed and worked upon.Ó 2015 Production and hosting by Elsevier B.V. on behalf

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“…The following is a brief summary of each of the algorithms, along with some simple business process examples to help explain their usage. The reader should refer to [4] for a more detailed description of the actual algorithms.…”

Section: Algorithmsmentioning

confidence: 99%

Graph-based approaches to insider threat detection

Eberle

Holder

2009

Proceedings of the 5th Annual Workshop on Cyber Security and Information Intelligence Research: Cyber Security and Information

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This work presents the use of graph-based approaches to discovering anomalous instances of structural patterns in data that represent entities, relationships and actions. Using the minimum description length (MDL) principle to first identify the normative pattern, the algorithms presented in this paper identify the three possible changes to a graph: modifications, insertions and deletions. Each algorithm discovers those substructures that match the closest to the normative pattern without matching exactly. As a result, this proposed approach searches for those activities that appear to match normal (or legitimate) transactions, but in fact are structurally different. After briefly presenting the three algorithms, we then show the usefulness of applying these graph theoretic approaches to discovering illegal activity for a simulated insider threat within a passport processing scenario.

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Anomaly detection in data represented as graphs

Cited by 87 publications

References 12 publications

A clique-based method for mining fuzzy graph patterns in anti-money laundering systems

A clique-based method for mining fuzzy graph patterns in anti-money laundering systems

A survey of data mining and social network analysis based anomaly detection techniques

Graph-based approaches to insider threat detection

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