Biblio

Internet of Things (IoT) devices have rooted themselves in the everyday life of billions of people. Thus, researchers have applied automated bug finding techniques to improve their overall security. However, due to the difficulties in extracting and emulating custom firmware, black-box fuzzing is often the only viable analysis option. Unfortunately, this solution mostly produces invalid inputs, which are quickly discarded by the targeted IoT device and do not penetrate its code. Another proposed approach is to leverage the companion app (i.e., the mobile app typically used to control an IoT device) to generate well-structured fuzzing inputs. Unfortunately, the existing solutions produce fuzzing inputs that are constrained by app-side validation code, thus significantly limiting the range of discovered vulnerabilities.In this paper, we propose a novel approach that overcomes these limitations. Our key observation is that there exist functions inside the companion app that can be used to generate optimal (i.e., valid yet under-constrained) fuzzing inputs. Such functions, which we call fuzzing triggers, are executed before any data-transforming functions (e.g., network serialization), but after the input validation code. Consequently, they generate inputs that are not constrained by app-side sanitization code, and, at the same time, are not discarded by the analyzed IoT device due to their invalid format. We design and develop Diane, a tool that combines static and dynamic analysis to find fuzzing triggers in Android companion apps, and then uses them to fuzz IoT devices automatically. We use Diane to analyze 11 popular IoT devices, and identify 11 bugs, 9 of which are zero days. Our results also show that without using fuzzing triggers, it is not possible to generate bug-triggering inputs for many devices.

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.