Extracting malicious behaviours

In recent years, the damage cost caused by malwares is huge. Thus, malware detection is a big challenge. The task of specifying malware takes a huge amount of time and engineering effort since it currently requires the manual study of the malicious code. Thus, in order to avoid the tedious manual analysis of malicious codes, this task has to be automatised. To this aim, we propose in this work to represent malicious behaviours using extended API call graphs , where nodes correspond to API function calls, edges specify the execution order between the API functions, and edge labels indicate the dependence relation between API functions parameters. We define new static analysis techniques that allow to extract such graphs from programs, and show how to automatically extract, from a set of malicious and benign programs, an extended API call graph that represents the malicious behaviours. Finally, we show how this graph can be used for malware detection. We implemented our techniques and obtained encouraging results: 95.66% of detection rate with 0% of false alarms.