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https://doi.org/10.1...arrow_drop_down
https://doi.org/10.1007/115078...
Part of book or chapter of book . 2005 . Peer-reviewed
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DBLP
Conference object . 2017
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Modeling and Preventing Phishing Attacks

Authors: Markus Jakobsson;

Modeling and Preventing Phishing Attacks

Abstract

A first contribution of this paper is a theoretical yet practically applicable model covering a large set of phishing attacks, aimed towards developing an understanding of threats relating to phishing. We model an attack by a phishing graph in which nodes correspond to knowledge or access rights, and (directed) edges correspond to means of obtaining information or access rights from already possessed information or access rights – whether this involves interaction with the victim or not. Edges may also be associated with probabilities, costs, or other measures of the hardness of traversing the graph. This allows us to quantify the effort of traversing a graph from some starting node (corresponding to publicly available information) to a target node that corresponds to access to a resource of the attacker’s choice. We discuss how to perform economic analysis on the viability of attacks. A quantification of the economical viability of various attacks allows a pinpointing of weak links for which improved security mechanisms would improve overall system security.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
48
Top 10%
Top 0.1%
Top 10%
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