Attack attribution in non-cooperative networks

Cohen, David S.; Narayanaswamy, Krishna

doi:10.1109/iaw.2004.1437851

Cited by 4 publications

(2 citation statements)

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“…. and the human actor(s) responsible for that behaviour of activity’ (Cohen and Narayanaswamy 2004, p. 52) are discovered and used to attribute the attack to a person. Authorship Analysis techniques have the ability to provide this level of attack attribution.…”

Section: Introductionmentioning

confidence: 99%

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Automated unsupervised authorship analysis using evidence accumulation clustering

Layton¹,

Watters²,

Dazeley³

2011

Nat. Lang. Eng.

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Authorship Analysis aims to extract information about the authorship of documents from features within those documents. Typically, this is performed as a classification task with the aim of identifying the author of a document, given a set of documents of known authorship. Alternatively, unsupervised methods have been developed primarily as visualisation tools to assist the manual discovery of clusters of authorship within a corpus by analysts. However, there is a need in many fields for more sophisticated unsupervised methods to automate the discovery, profiling and organisation of related information through clustering of documents by authorship. An automated and unsupervised methodology for clustering documents by authorship is proposed in this paper. The methodology is named NUANCE, for n-gram Unsupervised Automated Natural Cluster Ensemble. Testing indicates that the derived clusters have a strong correlation to the true authorship of unseen documents.

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Section: Introductionmentioning

confidence: 99%

“… 1 Level 1 attack attribution is the direct tracing of attacks through its attack path (Cohen and Narayanaswamy 2004). …”

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confidence: 99%