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Fast Auto Black Box Analysis With Infection Vector Identification

Malwares are released into the wild at a rapid rate daily. Over the years, malware has also become smarter to avoid detection attempts by malware analysts when performing static analysis. In terms of infection vector, there are more and more malwares with the capability to mask its infection vector....

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Bibliographic Details
Main Authors: Chanderan, Navien, Johari, Abdullah
Format: Magazine and Newsletter
Language:English
Published: Faculty of Computer Science and Information Technology 2015
Subjects:
A32 Universiti Malaysia Sarawak > Innovation.
Online Access:http://ir.unimas.my/id/eprint/8006/1/poster.pdf
http://ir.unimas.my/id/eprint/8006/
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spelling my.unimas.ir.80062016-04-12T02:42:21Z http://ir.unimas.my/id/eprint/8006/ Fast Auto Black Box Analysis With Infection Vector Identification Chanderan, Navien Johari, Abdullah A32 Universiti Malaysia Sarawak -- Innovation. Malwares are released into the wild at a rapid rate daily. Over the years, malware has also become smarter to avoid detection attempts by malware analysts when performing static analysis. In terms of infection vector, there are more and more malwares with the capability to mask its infection vector. At the rate of new malware being released into the wild and coupled the complexity of modern day malwares, analysts need to find a new way to work more efficiently. In this paper, a customized malware sandbox with the capability to identify the vector of infection is proposed to automate malware analysis by analyzing its behaviour and identifying its infection vector and also to reduce dependency on manual or static analysis. Faculty of Computer Science and Information Technology 2015-06-15 Magazine and Newsletter NonPeerReviewed text en http://ir.unimas.my/id/eprint/8006/1/poster.pdf Chanderan, Navien and Johari, Abdullah (2015) Fast Auto Black Box Analysis With Infection Vector Identification. [Magazine and Newsletter] (Unpublished)
institution Universiti Malaysia Sarawak
building Centre for Academic Information Services (CAIS)
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Sarawak
content_source UNIMAS Institutional Repository
url_provider http://ir.unimas.my/
language English
topic A32 Universiti Malaysia Sarawak -- Innovation.
spellingShingle A32 Universiti Malaysia Sarawak -- Innovation.
Chanderan, Navien
Johari, Abdullah
Fast Auto Black Box Analysis With Infection Vector Identification
description Malwares are released into the wild at a rapid rate daily. Over the years, malware has also become smarter to avoid detection attempts by malware analysts when performing static analysis. In terms of infection vector, there are more and more malwares with the capability to mask its infection vector. At the rate of new malware being released into the wild and coupled the complexity of modern day malwares, analysts need to find a new way to work more efficiently. In this paper, a customized malware sandbox with the capability to identify the vector of infection is proposed to automate malware analysis by analyzing its behaviour and identifying its infection vector and also to reduce dependency on manual or static analysis.
format Magazine and Newsletter
author Chanderan, Navien
Johari, Abdullah
author_facet Chanderan, Navien
Johari, Abdullah
author_sort Chanderan, Navien
title Fast Auto Black Box Analysis With Infection Vector Identification
title_short Fast Auto Black Box Analysis With Infection Vector Identification
title_full Fast Auto Black Box Analysis With Infection Vector Identification
title_fullStr Fast Auto Black Box Analysis With Infection Vector Identification
title_full_unstemmed Fast Auto Black Box Analysis With Infection Vector Identification
title_sort fast auto black box analysis with infection vector identification
publisher Faculty of Computer Science and Information Technology
publishDate 2015
url http://ir.unimas.my/id/eprint/8006/1/poster.pdf
http://ir.unimas.my/id/eprint/8006/
_version_ 1644510432272056320
score 13.211869

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