Thread Graphs for Visualizing Malware Behavior
Tuesday, August 25. 2009
The last blog post dealt with our recent research on visualizing malware behavior. Now a quick update on the thread graphs we generate for visualizing malware behavior: since tree maps display nothing about the sequence of operations, we use another presentation format to visualize the temporal behavior of the individual threads of a sample. A thread graph can be regarded as a behavioral fingerprint of the sample that represents the temporal order of executed system commands and the different threads spawned by a binary. The x-axis represents the time (sequence of performed actions), while the y-axis indicates the operation/section of the performed action. An analyst can then study this behavior graph to quickly learn more about the actions of each individual thread.
The following two pictures show examples of this kind of visualization:
On the left hand picture, we can see that one thread is responsible for the majority of operations for the sample. This thread performs many registry operations and initially performs many network- and system-related operations (operations 90-140). Additionally, two more threads are spawned, but they perform only a limited amount of operations during the analysis phase. The thread graph for the malware sample on the right side is completely different and an analyst can get a quick overview of what actions a given samples performs.
The following two pictures show examples of this kind of visualization:
On the left hand picture, we can see that one thread is responsible for the majority of operations for the sample. This thread performs many registry operations and initially performs many network- and system-related operations (operations 90-140). Additionally, two more threads are spawned, but they perform only a limited amount of operations during the analysis phase. The thread graph for the malware sample on the right side is completely different and an analyst can get a quick overview of what actions a given samples performs.
