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Classification of Spoofing Attack Types

Authors: J. Rossouw van der Merwe; Xabier Zubizarreta; Ivana Lukcin; Alexander Rugamer; Wolfgang Felber;

Classification of Spoofing Attack Types

Abstract

All spoofer attacks have different requirements, impacts, success rates and objectives; therefore, to assess the threat and to develop appropriate counter measures, a clear classification is needed. Being aware of the different existing types of threats, allows an improved design of preventative measures to counter these attacks. This paper classifies spoofer attacks with a layered model. This allows assessing the risks and strategies of operational spoofers with the goal of prevention. The layered model consists of the deployment architectures, the take-over strategy, the control strategy and the application. The paper expands the strategies to manipulate a position of receiver, highlights operational difficulties and suitable counter measures. This emphasises that even if a signal is successfully spoofed, controlling a target receiver is not trivial. Additionally, the most probable spoofing attacks are presented and the applicable antispoofing methods are outlined.

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    popularity
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    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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Powered by OpenAIRE graph
Found an issue? Give us feedback
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!
33
Top 10%
Top 10%
Top 10%
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