Séminaire APRRSS

Séminaire APR: On static malware detection


24/02/2017
Intervenant(s) : Tayssir Touili (LIPN, Paris, France)
The number of malware is growing extraordinarily fast. A malware may bring serious damage, e.g., the worm MyDoom slowed down global internet access by ten percent in 2004. Thus, it is crucial to have efficient up-to-date virus detectors. Existing antivirus systems use various detection techniques to identify viruses such as (1) code emulation where the virus is executed in a virtual environment to get detected; or (2) signature detection, where a signature is a pattern of program code that characterizes the virus. A file is declared as a virus if it contains a sequence of binary code instructions that matches one of the known signatures.
These techniques are becoming insufficient. Indeed, emulation based techniques can only check the program's behavior in a limited time interval. As for signature based systems, it is very easy to virus developers to get around them. Thus, a robust malware detection technique needs to check the behavior (not the syntax) of the program without executing it. We show in this talk how using behavior signatures allow to efficiently detect malwares in a completely static way. We implemented our techniques in a tool, and we applied it to detect several viruses. Our results are encouraging. In particular, our tool was able to detect more than 800 viruses. Several of these viruses could not be detected by well-known anti-viruses such as Avira, Avast, Norton, Kaspersky and McAfee.
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