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Behavior-based cyber security: Using...

Grana, Justin.

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Behavior-based cyber security: Using traditional and behavioral game theory to improve attack detection.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Behavior-based cyber security: Using traditional and behavioral game theory to improve attack detection./
Author:	Grana, Justin.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2016,
Description:	125 p.
Notes:	Source: Dissertation Abstracts International, Volume: 77-12(E), Section: A.
Contained By:	Dissertation Abstracts International77-12A(E).
Subject:	Economic theory. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10144198
ISBN:	9781339993607

Behavior-based cyber security: Using traditional and behavioral game theory to improve attack detection.
Grana, Justin.

Behavior-based cyber security: Using traditional and behavioral game theory to improve attack detection. - Ann Arbor : ProQuest Dissertations & Theses, 2016 - 125 p.

Source: Dissertation Abstracts International, Volume: 77-12(E), Section: A.

Thesis (Ph.D.)--American University, 2016.

It is undeniable that computer network security is of paramount importance. As unauthorized users increase in sophistication, it is necessary to develop tools to not only respond to their new methods but to anticipate the attack methods before they are deployed. This dissertation will investigate the possibility of employing traditional and behavioral game theory to improve intrusion detection within a computer network as opposed to at a network's firewall. It will first show how to incorporate knowledge of an attacker's behavior to improve detection within a network's perimeter. The second contribution will be to establish a new strategic foundation for attackers and defenders that includes an explicit model of the signal classification problem faced by the defender. This inclusion purifies the mixed-strategy equilibrium results that are ubiquitous in strategic analyses of computer network attack. The final contribution will be to synthesize the statistical method of improving attack detection and the strategic intuition to analyze how boundedly rational (in the form of level-k thinkers) defenders and attackers interact in a noisy network environment. The results show that specifying exactly when and at which hosts an attacker will attack leaves the defender vulnerable to attacks at other "unmonitored" locations. However, if the defender assumes an attacker will exploit all vulnerabilities, then he can optimally define a likelihood ratio attack detector that anticipates the attacker's strategy.

ISBN: 9781339993607Subjects--Topical Terms:

1556984
Economic theory.

Behavior-based cyber security: Using traditional and behavioral game theory to improve attack detection.
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245 1 0 $a Behavior-based cyber security: Using traditional and behavioral game theory to improve attack detection.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2016
300 $a 125 p.
500 $a Source: Dissertation Abstracts International, Volume: 77-12(E), Section: A.
500 $a Adviser: Alan Isaac.
502 $a Thesis (Ph.D.)--American University, 2016.
520 $a It is undeniable that computer network security is of paramount importance. As unauthorized users increase in sophistication, it is necessary to develop tools to not only respond to their new methods but to anticipate the attack methods before they are deployed. This dissertation will investigate the possibility of employing traditional and behavioral game theory to improve intrusion detection within a computer network as opposed to at a network's firewall. It will first show how to incorporate knowledge of an attacker's behavior to improve detection within a network's perimeter. The second contribution will be to establish a new strategic foundation for attackers and defenders that includes an explicit model of the signal classification problem faced by the defender. This inclusion purifies the mixed-strategy equilibrium results that are ubiquitous in strategic analyses of computer network attack. The final contribution will be to synthesize the statistical method of improving attack detection and the strategic intuition to analyze how boundedly rational (in the form of level-k thinkers) defenders and attackers interact in a noisy network environment. The results show that specifying exactly when and at which hosts an attacker will attack leaves the defender vulnerable to attacks at other "unmonitored" locations. However, if the defender assumes an attacker will exploit all vulnerabilities, then he can optimally define a likelihood ratio attack detector that anticipates the attacker's strategy.
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