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....
| Main Authors: | , |
|---|---|
| Format: | |
| Language: | English |
| Published: |
Faculty of Computer Science and Information Technology
2015
|
| Subjects: | |
| Online Access: | http://ir.unimas.my/id/eprint/8006/ http://ir.unimas.my/id/eprint/8006/1/poster.pdf |
| _version_ | 1848836278356803584 |
|---|---|
| author | Chanderan, Navien Johari, Abdullah |
| author_facet | Chanderan, Navien Johari, Abdullah |
| author_sort | Chanderan, Navien |
| building | UNIMAS Institutional Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-15T06:21:13Z |
| format | Magazine and Newsletter |
| id | unimas-8006 |
| institution | Universiti Malaysia Sarawak |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T06:21:13Z |
| publishDate | 2015 |
| publisher | Faculty of Computer Science and Information Technology |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | unimas-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) |
| spellingShingle | A32 Universiti Malaysia Sarawak -- Innovation. Chanderan, Navien Johari, Abdullah Fast Auto Black Box Analysis With Infection Vector Identification |
| title | 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_short | Fast Auto Black Box Analysis With Infection Vector
Identification |
| title_sort | fast auto black box analysis with infection vector
identification |
| topic | A32 Universiti Malaysia Sarawak -- Innovation. |
| url | http://ir.unimas.my/id/eprint/8006/ http://ir.unimas.my/id/eprint/8006/1/poster.pdf |