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http://eprint.iacr.org/2005/29...
Part of book or chapter of book
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IEEE Transactions on Dependable and Secure Computing
Article . 2007 . Peer-reviewed
License: IEEE Copyright
Data sources: Crossref
https://doi.org/10.1007/115619...
Part of book or chapter of book . 2005 . Peer-reviewed
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Article . 2005
Data sources: DBLP
DBLP
Article . 2007
Data sources: DBLP
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Keeping Denial-of-Service Attackers in the Dark

Authors: Gal Badishi; Amir Herzberg; Idit Keidar;

Keeping Denial-of-Service Attackers in the Dark

Abstract

We consider the problem of overcoming (distributed) denial-of-service (DoS) attacks by realistic adversaries that have knowledge of their attack's successfulness, for example, by observing service performance degradation or by eavesdropping on messages or parts thereof. A solution for this problem in a high-speed network environment necessitates lightweight mechanisms for differentiating between valid traffic and the attacker's packets. The main challenge in presenting such a solution is to exploit existing packet-filtering mechanisms in a way that allows fast processing of packets but is complex enough so that the attacker cannot efficiently craft packets that pass the filters. We show a protocol that mitigates DoS attacks by adversaries that can eavesdrop and (with some delay) adapt their attacks accordingly. The protocol uses only available efficient packet-filtering mechanisms based mainly on addresses and port numbers. Our protocol avoids the use of fixed ports and instead performs "pseudorandom port hopping." We model the underlying packet-filtering services and define measures for the capabilities of the adversary and for the success rate of the protocol. Using these, we provide a novel rigorous analysis of the impact of DoS on an end-to-end protocol and show that our protocol provides effective DoS prevention for realistic attack and deployment scenarios.

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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).
    56
    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.
    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).
    Top 1%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
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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!
56
Top 10%
Top 1%
Average
bronze