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Detecting network attacks using behavioural models

Authors: Jiri Schafer; Michal Drozd;

Detecting network attacks using behavioural models

Abstract

In this paper we're dealing with the problem of detecting malware using behaviour model. For better malware description we have divided this model into two parts — malware spreading model and malware statistical behavioural model. Spreading models are typical epidemiological models like SI model, advanced SIR and SEIR models and empiric file spreading model. In statistical behavioural model we're describing characteristics of malware trojan communication and communication characteristics of a typical user, we're describing basic detection for both models (behavioural statistic and spreading), we're proposing some standard and specific countermeasures based on these models as same as possibility of detection of malware communication, attacks like DoS and Network scanning detection and detection of Malware propagation.

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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!
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Average
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