國立虎尾科技大學 |

Detecting Mimicry Attacks in Windows Malware.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Detecting Mimicry Attacks in Windows Malware./
作者:	Yin, Haikuo.
面頁冊數:	1 online resource (188 pages)
附註:	Source: Dissertations Abstracts International, Volume: 85-06, Section: B.
Contained By:	Dissertations Abstracts International85-06B.
標題:	Computer engineering. -
電子資源:	click for full text (PQDT)
ISBN:	9798381167368

Detecting Mimicry Attacks in Windows Malware.
Yin, Haikuo.

Detecting Mimicry Attacks in Windows Malware. - 1 online resource (188 pages)

Source: Dissertations Abstracts International, Volume: 85-06, Section: B.

Thesis (Ph.D.)--University of California, Los Angeles, 2023.

Includes bibliographical references

Ever since the earliest days of the Internet, malware has been a problem for computers. Since then, this problem's severity has only increased, with important organizations like universities and hospitals suffering major security breaches due to malware. As detection techniques get more advanced, so do attackers' evasion attempts. One such method, called the mimicry attack, introduces benign behavior to malware to produce a benign classification in detectors even while retaining its malicious behaviors. In this document, I will describe the work we did on developing malware detection methods that remain effective in the presence of such evasion attacks. Using Windows APIs, our detection pipeline generates a summary of program behavior, vectorizes it in a way that's robust to modifications, and constrains features to reduce the impact of added benign behaviors. We use two methods of constraining features, enforcing monotonicity on them and removing them from the feature vector. To evaluate this detection pipeline and other methods, we use hooking and injection techniques to generate mimicry attacks that can insert benign behavior in more locations than prior work, and are thus produce stronger attacks than prior work. Our results show that our methods can effectively and consistently detect malware that use both mimicry attacks and adversarial attacks with minimal accuracy loss in vanilla data.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798381167368Subjects--Topical Terms:

569006
Computer engineering.
Subjects--Index Terms:

Adversarial attackIndex Terms--Genre/Form:

554714
Electronic books.

Detecting Mimicry Attacks in Windows Malware.
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520 $a Ever since the earliest days of the Internet, malware has been a problem for computers. Since then, this problem's severity has only increased, with important organizations like universities and hospitals suffering major security breaches due to malware. As detection techniques get more advanced, so do attackers' evasion attempts. One such method, called the mimicry attack, introduces benign behavior to malware to produce a benign classification in detectors even while retaining its malicious behaviors. In this document, I will describe the work we did on developing malware detection methods that remain effective in the presence of such evasion attacks. Using Windows APIs, our detection pipeline generates a summary of program behavior, vectorizes it in a way that's robust to modifications, and constrains features to reduce the impact of added benign behaviors. We use two methods of constraining features, enforcing monotonicity on them and removing them from the feature vector. To evaluate this detection pipeline and other methods, we use hooking and injection techniques to generate mimicry attacks that can insert benign behavior in more locations than prior work, and are thus produce stronger attacks than prior work. Our results show that our methods can effectively and consistently detect malware that use both mimicry attacks and adversarial attacks with minimal accuracy loss in vanilla data.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2024
538 $a Mode of access: World Wide Web
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653 $a Malware detection
653 $a Mimicry attack
653 $a Monotonic model
653 $a Windows API
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