國立虎尾科技大學 |

Leveraging machine learning for security related decision making.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Leveraging machine learning for security related decision making./
Author:	Gates, Christopher S.
Description:	1 online resource (135 pages)
Notes:	Source: Dissertation Abstracts International, Volume: 76-05(E), Section: B.
Contained By:	Dissertation Abstracts International76-05B(E).
Subject:	Computer science. -
Online resource:	click for full text (PQDT)
ISBN:	9781321446814

Leveraging machine learning for security related decision making.
Gates, Christopher S.

Leveraging machine learning for security related decision making. - 1 online resource (135 pages)

Source: Dissertation Abstracts International, Volume: 76-05(E), Section: B.

Thesis (Ph.D.)--Purdue University, 2014.

Includes bibliographical references

The need to ensure the primary functionality of any system means that considerations of security are often secondary. Computer security considerations are made in relation to considerations of usability, functionality, productivity, and other goals. Decision-making related to security is about finding an appropriate tradeoff. Most existing security mechanisms take a binary approach where an action is either malicious or benign, and therefore allowed or denied. However, security and privacy outcomes are often fuzzy and cannot be represented by a binary decision. It is useful for end users, who may ultimately need to allow or deny an action, to understand the potential differences among objects and the way that these differences are communicated matters.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9781321446814Subjects--Topical Terms:

573171
Computer science.
Index Terms--Genre/Form:

554714
Electronic books.

Leveraging machine learning for security related decision making.
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245 1 0 $a Leveraging machine learning for security related decision making.
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500 $a Source: Dissertation Abstracts International, Volume: 76-05(E), Section: B.
500 $a Adviser: Ninghui Li.
502 $a Thesis (Ph.D.)--Purdue University, 2014.
504 $a Includes bibliographical references
520 $a The need to ensure the primary functionality of any system means that considerations of security are often secondary. Computer security considerations are made in relation to considerations of usability, functionality, productivity, and other goals. Decision-making related to security is about finding an appropriate tradeoff. Most existing security mechanisms take a binary approach where an action is either malicious or benign, and therefore allowed or denied. However, security and privacy outcomes are often fuzzy and cannot be represented by a binary decision. It is useful for end users, who may ultimately need to allow or deny an action, to understand the potential differences among objects and the way that these differences are communicated matters.
520 $a In this work, we use machine learning and feature extraction techniques to model normal behavior in various contexts and then used those models to detect the degree that new behavior is anomalous. This measurement can then be used, not as a binary signal but as a more nuanced indicator that can be communicated to a user to help guide decision-making.
520 $a We examine the application of this idea in two domains. The first is the installation of applications on a mobile device. The focus in this domain is on permissions that represent capabilities and access to data, and we generate a model for expected permission requests. Various user studies were conducted to explore effective ways to communicate this measurement to influence decision-making by end users. Next, we examined to the domain of insider threat detection in the setting of a source code repository. The goal was to build models of expected user access and more appropriately predict the degree that new behavior deviates from the previous behavior. This information can be utilized and understood by security personnel to focus on unexpected patterns.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Computer science. $3 573171
650 4 $a Artificial intelligence. $3 559380
655 7 $a Electronic books. $2 local $3 554714
690 $a 0984
690 $a 0800
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a Purdue University. $b Computer Sciences. $3 1179390
773 0 $t Dissertation Abstracts International $g 76-05B(E).
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