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image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Computerarrow_drop_down
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
Computer
Article . 1997 . Peer-reviewed
License: IEEE Copyright
Data sources: Crossref
DBLP
Article . 2020
Data sources: DBLP
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Detecting attacks on networks

Authors: Chris Herringshaw;

Detecting attacks on networks

Abstract

As Internet based and intranet based network systems have evolved, they have become invaluable tools that businesses can use to share information and conduct business with online partners. However, hackers have also learned to use these systems to access private networks and their resources. Studies have shown that many organizations have suffered external and internal network intrusions. Internet systems are subject to various types of attacks. Traditional network security products, such as firewalls, can be penetrated from outside and can also leave organizations vulnerable to internal attacks. Generally, victims do not find out that their networks have been attacked until they examine system logs the next day, after the damage has been done. Network intrusion detection systems solve this problem by detecting external and internal security breaches as they happen and immediately notifying security personnel and network administrators by e mail or pager. Intrusion detection systems use several types of algorithms to detect possible security breaches, including algorithms for statistical anomaly detection, rule based anomaly detection, and a hybrid of the two.

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    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).
    20
    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.
    Average
    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 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
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!
20
Average
Top 10%
Average
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