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Using Deceptive Information in Computer Security Defenses

Authors: Mohammed H. Almeshekah; Eugene H. Spafford;

Using Deceptive Information in Computer Security Defenses

Abstract

In this paper the authors present a novel taxonomy of methods and techniques that can be used to protect digital information. The authors discuss how information has been protected and show how we can structure our methods to achieve better results. They explore the relationships among these protection techniques grouped into four categories: denial and isolation, degradation and obfuscation, negative information and deception and adversary attribution and counter-operations. The authors discuss how can they be applied at different scales within organizations. They map these protection techniques against the cyber kill-chain model and discuss some findings. Moreover, they identify the use of deceit as a useful protection technique that can significantly enhance the security of computer systems. They posit how the well-known Kerckhoff's principle has been misinterpreted to drive the security community away from deception-based mechanisms. The authors examine advantages these techniques can have when protecting our information in addition to traditional methods of denial and hardening. They show that by intelligently introducing deceit in information systems, the authors not only lead attackers astray, but also give organizations the ability to detect leakage; create doubt and uncertainty in leaked data; add risk at the adversaries' side to using the leaked information; and significantly enhance our abilities to attribute adversaries. They discuss how to overcome some of the challenges that hinder the adoption of deception-based techniques and present some recent work, their own contribution, and some promising directions for future research.

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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!
2
Average
Average
Average
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