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IEEE/ACM Transactions on Networking
Article . 2007 . Peer-reviewed
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Article . 2007
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On Scalable Attack Detection in the Network

Authors: Ramana Rao Kompella; Sumeet Singh; George Varghese;

On Scalable Attack Detection in the Network

Abstract

Current intrusion detection and prevention systems seek to detect a wide class of network intrusions (e.g., DoS attacks, worms, port scans)at network vantage points. Unfortunately, all the IDS systems we know of keep per-connection or per-flow state. Thus it is hardly surprising that IDS systems (other than signature detection mechanisms) have not scaled to multi-gigabit speeds. By contrast, note that both router lookups and fair queuing have scaled to high speeds using aggregation via prefix lookups or DiffServ. Thus in this paper, we initiate research into the question as to whether one can detect attacks without keeping per-flow state. We will show that such aggregation, while making fast implementations possible, immediately cause two problems. First, aggregation can cause behavioral aliasing where, for example, good behaviors can aggregate to look like bad behaviors. Second, aggregated schemes are susceptible to spoofing by which the intruder sends attacks that have appropriate aggregate behavior. We examine a wide variety of DoS attacks and show that several categories (bandwidth based, claim-and-hold, host scanning) can be scalably detected. By contrast, it appears that stealthy port-scanning cannot be scalably detected without keeping per-flow state.

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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!
97
Top 10%
Top 1%
Top 10%
bronze