Fast Auto Black Box Analysis With Infection Vector Identification

• 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/

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.