語系:
繁體中文
English
說明(常見問題)
登入
回首頁
切換:
標籤
|
MARC模式
|
ISBD
Securing Cyberspace : = AI-Enabled Cyber-Adversary Defense.
紀錄類型:
書目-語言資料,手稿 : Monograph/item
正題名/作者:
Securing Cyberspace :/
其他題名:
AI-Enabled Cyber-Adversary Defense.
作者:
Ampel, Benjamin.
面頁冊數:
1 online resource (178 pages)
附註:
Source: Dissertations Abstracts International, Volume: 85-11, Section: B.
Contained By:
Dissertations Abstracts International85-11B.
標題:
Computer science. -
電子資源:
click for full text (PQDT)
ISBN:
9798382563770
Securing Cyberspace : = AI-Enabled Cyber-Adversary Defense.
Ampel, Benjamin.
Securing Cyberspace :
AI-Enabled Cyber-Adversary Defense. - 1 online resource (178 pages)
Source: Dissertations Abstracts International, Volume: 85-11, Section: B.
Thesis (Ph.D.)--The University of Arizona, 2024.
Includes bibliographical references
The proliferation of information technology (IT) has transformed modern society. However, IT has created significant vulnerabilities owing to its rapid development cycle. Adversaries can exploit these vulnerabilities using assets (the set of tools and techniques used by adversaries to conduct advanced cyber-attacks) to gain access to an organization's critical assets, networks, systems, and confidential data. Existing procedures for identifying, collecting, mitigating, and robustifying adversarial assets are often manual. Cyber analysts often cite these manual processes as their primary challenges. Furthermore, the cyber threat intelligence (CTI) provided by these procedures is often reactive after a breach has occurred. Therefore, automating proactive collection, identification, mitigation, and robustification against adversarial assets is critical for proactive CTI and for improving organizational cybersecurity.This dissertation proposes four essays that utilize state-of-the-art deep learning techniques to automate the analysis of adversary assets and enhance CTI. Essay I uses text classification with deep transfer learning to categorize adversary assets based on their attack vectors. Essay II adopts principles of text classification with multi-teacher knowledge distillation to link adversary assets to mitigation strategies in the MITRE ATT&CK framework. Essay III leverages text generation and adversarial training to robustify AI models against adversarial assets. Finally, Essay IV adopts AI-enabled audio generation and classification techniques to protect against adversarial assets in the audio domain. All four essays contribute significant practical implications and add to the information systems knowledge base. By automating and improving adversary asset analysis, this research can provide organizations with a proactive approach to identifying, mitigating, and robustifying against adversarial assets.
Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024
Mode of access: World Wide Web
ISBN: 9798382563770Subjects--Topical Terms:
573171
Computer science.
Subjects--Index Terms:
Cyber threat intelligenceIndex Terms--Genre/Form:
554714
Electronic books.
Securing Cyberspace : = AI-Enabled Cyber-Adversary Defense.
LDR
:03359ntm a22004217 4500
001
1145003
005
20240617111738.5
006
m o d
007
cr mn ---uuuuu
008
250605s2024 xx obm 000 0 eng d
020
$a
9798382563770
035
$a
(MiAaPQ)AAI31240502
035
$a
AAI31240502
040
$a
MiAaPQ
$b
eng
$c
MiAaPQ
$d
NTU
100
1
$a
Ampel, Benjamin.
$3
1470202
245
1 0
$a
Securing Cyberspace :
$b
AI-Enabled Cyber-Adversary Defense.
264
0
$c
2024
300
$a
1 online resource (178 pages)
336
$a
text
$b
txt
$2
rdacontent
337
$a
computer
$b
c
$2
rdamedia
338
$a
online resource
$b
cr
$2
rdacarrier
500
$a
Source: Dissertations Abstracts International, Volume: 85-11, Section: B.
500
$a
Advisor: Chen, Hsinchun.
502
$a
Thesis (Ph.D.)--The University of Arizona, 2024.
504
$a
Includes bibliographical references
520
$a
The proliferation of information technology (IT) has transformed modern society. However, IT has created significant vulnerabilities owing to its rapid development cycle. Adversaries can exploit these vulnerabilities using assets (the set of tools and techniques used by adversaries to conduct advanced cyber-attacks) to gain access to an organization's critical assets, networks, systems, and confidential data. Existing procedures for identifying, collecting, mitigating, and robustifying adversarial assets are often manual. Cyber analysts often cite these manual processes as their primary challenges. Furthermore, the cyber threat intelligence (CTI) provided by these procedures is often reactive after a breach has occurred. Therefore, automating proactive collection, identification, mitigation, and robustification against adversarial assets is critical for proactive CTI and for improving organizational cybersecurity.This dissertation proposes four essays that utilize state-of-the-art deep learning techniques to automate the analysis of adversary assets and enhance CTI. Essay I uses text classification with deep transfer learning to categorize adversary assets based on their attack vectors. Essay II adopts principles of text classification with multi-teacher knowledge distillation to link adversary assets to mitigation strategies in the MITRE ATT&CK framework. Essay III leverages text generation and adversarial training to robustify AI models against adversarial assets. Finally, Essay IV adopts AI-enabled audio generation and classification techniques to protect against adversarial assets in the audio domain. All four essays contribute significant practical implications and add to the information systems knowledge base. By automating and improving adversary asset analysis, this research can provide organizations with a proactive approach to identifying, mitigating, and robustifying against adversarial assets.
533
$a
Electronic reproduction.
$b
Ann Arbor, Mich. :
$c
ProQuest,
$d
2024
538
$a
Mode of access: World Wide Web
650
4
$a
Computer science.
$3
573171
650
4
$a
Computer engineering.
$3
569006
650
4
$a
Information technology.
$3
559429
653
$a
Cyber threat intelligence
653
$a
Cybersecurity
653
$a
Deep learning
653
$a
Design science
653
$a
Hacker communities
653
$a
Large Language Models
655
7
$a
Electronic books.
$2
local
$3
554714
690
$a
0489
690
$a
0984
690
$a
0464
690
$a
0800
710
2
$a
The University of Arizona.
$b
Management Information Systems.
$3
1179532
710
2
$a
ProQuest Information and Learning Co.
$3
1178819
773
0
$t
Dissertations Abstracts International
$g
85-11B.
856
4 0
$u
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31240502
$z
click for full text (PQDT)
筆 0 讀者評論
多媒體
評論
新增評論
分享你的心得
Export
取書館別
處理中
...
變更密碼[密碼必須為2種組合(英文和數字)及長度為10碼以上]
登入