Malware Detection Via Hybrid Analysis for API Calls
Taewoo Kang, Jae-Ik Cho, Manhyun Chung, Jongsub Moon · Information Security and Cryptology · 2007
We have come a long way in the information age. Thanks to the advancement of such technologies as the internet, we have discovered new ways to convey information on a broader scope. However, negative aspects exist as is with anything else. These may include invasion of privacy over the web, or identity theft over the internet. What is more alarming is that malwares so called 'maliciouscodes' are rapidly spreading. Its intent is very destructive which can result in hacking, phishing and as aforementioned, one of the most disturbing problems on the net, invasion of privacy. This thesis describes the technology of how you can effectively analyze and detect these kind of malicious codes. We propose sequencial hybrid analysis for API calls that are hooked inside user-mode and kernel-level of Windows. This research explains how we can cope with malicious code more efficiently by abstracting malicious function signature and hiding attribute.