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Maggi, Federico, Balduzzi, Marco, Flores, Ryan, Gu, Lion, Ciancaglini, Vincenzo.  2018.  Investigating Web Defacement Campaigns at Large. Proceedings of the 2018 on Asia Conference on Computer and Communications Security. :443–456.
Website defacement is the practice of altering the web pages of a website after its compromise. The altered pages, calleddeface pages, can negatively affect the reputation and business of the victim site. Previous research has focused primarily on detection, rather than exploring the defacement phenomenon in depth. While investigating several defacements, we observed that the artifacts left by the defacers allow an expert analyst to investigate the actors' modus operandi and social structure, and expand from the single deface page to a group of related defacements (i.e., acampaign ). However, manually performing such analysis on millions of incidents is tedious, and poses scalability challenges. From these observations, we propose an automated approach that efficiently builds intelligence information out of raw deface pages. Our approach streamlines the analysts job by automatically recognizing defacement campaigns, and assigning meaningful textual labels to them. Applied to a comprehensive dataset of 13 million defacement records, from Jan. 1998 to Sept. 2016, our approach allowed us to conduct the first large-scale measurement on web defacement campaigns. In addition, our approach is meant to be adopted operationally by analysts to identify live campaigns on the field. We go beyond confirming anecdotal evidence. We analyze the social structure of modern defacers, which includes lone individuals as well as actors that cooperate with each others, or with teams, which evolve over time and dominate the scene. We conclude by drawing a parallel between the time line of World-shaping events and defacement campaigns, representing the evolution of the interests and orientation of modern defacers.
Continella, Andrea, Guagnelli, Alessandro, Zingaro, Giovanni, De Pasquale, Giulio, Barenghi, Alessandro, Zanero, Stefano, Maggi, Federico.  2016.  ShieldFS: A Self-healing, Ransomware-aware Filesystem. Proceedings of the 32Nd Annual Conference on Computer Security Applications. :336–347.

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.