Biblio

The ever-increasing number of malware samples demands for automated tools that aid the analysts in the reverse engineering of complex malicious binaries. Frequently, malware communicates over an encrypted channel with external network resources under the control of malicious actors, such as Command and Control servers that control the botnet of infected machines. Hence, a key aspect in malware analysis is uncovering and understanding the semantics of network communications. In this paper we present SysTaint, a semi-automated tool that runs malware samples in a controlled environment and analyzes their execution to support the analyst in identifying the functions involved in the communication and the exchanged data. Our evaluation on four banking Trojan samples from different families shows that SysTaint is able to handle and inspect encrypted network communications, obtaining useful information on the data being sent and received, on how each sample processes this data, and on the inner portions of code that deal with the data processing.

Preventive and reactive security measures can only partially mitigate the damage caused by modern ransomware attacks. Indeed, the remarkable amount of illicit profit and the cyber-criminals' increasing interest in ransomware schemes suggest that a fair number of users are actually paying the ransoms. Unfortunately, pure-detection approaches (e.g., based on analysis sandboxes or pipelines) are not sufficient nowadays, because often we do not have the luxury of being able to isolate a sample to analyze, and when this happens it is already too late for several users! We believe that a forward-looking solution is to equip modern operating systems with practical self-healing capabilities against this serious threat. Towards such a vision, we propose ShieldFS, an add-on driver that makes the Windows native filesystem immune to ransomware attacks. For each running process, ShieldFS dynamically toggles a protection layer that acts as a copy-on-write mechanism, according to the outcome of its detection component. Internally, ShieldFS monitors the low-level filesystem activity to update a set of adaptive models that profile the system activity over time. Whenever one or more processes violate these models, their operations are deemed malicious and the side effects on the filesystem are transparently rolled back. We designed ShieldFS after an analysis of billions of low-level, I/O filesystem requests generated by thousands of benign applications, which we collected from clean machines in use by real users for about one month. This is the first measurement on the filesystem activity of a large set of benign applications in real working conditions. We evaluated ShieldFS in real-world working conditions on real, personal machines, against samples from state of the art ransomware families. ShieldFS was able to detect the malicious activity at runtime and transparently recover all the original files. Although the models can be tuned to fit various filesystem usage profiles, our results show that our initial tuning yields high accuracy even on unseen samples and variants.