Christian van Sloun (RWTH Aachen University), Vincent Woeste (RWTH Aachen University), Konrad Wolsing (RWTH Aachen University & Fraunhofer FKIE), Jan Pennekamp (RWTH Aachen University), Klaus Wehrle (RWTH Aachen University)
Ransomware attacks have become one of the most widely feared cyber attacks for businesses and home users.
Since attacks are evolving and use advanced phishing campaigns and zero-day exploits, everyone is at risk, ranging from novice users to experts.
As a result, much research has focused on preventing and detecting ransomware attacks, with real-time monitoring of I/O activity being the most prominent approach for detection.
These approaches have in common that they inject code into the execution of the operating system's I/O stack, a more and more optimized system.
However, they seemingly do not consider the impact the integration of such mechanisms would have on system performance or only consider slow storage mediums, such as rotational hard disk drives.
This paper analyzes the impact of monitoring different features of relevant I/O operations for Windows and Linux.
We find that even simple features, such as the entropy of a buffer, can increase execution time by 350% and reduce SSD performance by up to 75%.
To combat this degradation, we propose adjusting the number of monitored features based on a process's behavior in real-time.
To this end, we design and implement a multi-staged IDS that can adjust overhead by moving a process between stages that monitor different numbers of features.
By moving seemingly benign processes to stages with fewer features and less overhead while moving suspicious processes to stages with more features to confirm the suspicion, the average time a system requires to perform I/O operations can be reduced drastically.
We evaluate the effectiveness of our design by combining actual I/O behavior from a public dataset with the measurements we gathered for each I/O operation and found that a multi-staged design can reduce the overhead to I/O operations by an order of magnitude while maintaining similar detection accuracy of traditional single-staged approaches.
As a result, real-time behavior monitoring for ransomware detection becomes feasible despite its inherent overhead impacts.