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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 https://doi.org/10.1...arrow_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
https://doi.org/10.1109/cais.2...
Article . 2018 . Peer-reviewed
License: STM Policy #29
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Intrusion and ransomware detection system

Authors: Marianne A. Azer; Ahmed El-Kosairy;

Intrusion and ransomware detection system

Abstract

Attackers and cybercriminals are always in a race to either compromise networks and servers or embezzle ransoms through ransomware. Intruders must be prevented from such exploitations of assets, and their malicious attempts counterattacked. Among of the easiest ways of preventing intruders from compromising servers and networks is the use of traditional security controls, such as Intrusion Prevention Systems (IPS), firewalls and Anti-viruses. Such tactics could be successful at lower attacks levels. Current attacks are more aggressive, they can bypass most security tools. Servers are being compromised and files encrypted for ransom. In this paper, we introduce layers of deception systems to detect any intrusion or ransomware trying to gain access to compromise private files by using a deception system based on honeyfiles and honeytokens. We deploy a proof of concept implementation of one of the key deception methods proposed to detect ransomware and intruders.

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    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.
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citations
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!
12
Top 10%
Top 10%
Top 10%
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