We present a new side-channel attack that targets one of the most fundamental software caches in modern computer systems: the operating system page cache. The page cache is a pure software cache that contains all disk-backed pages, including program binaries, shared libraries, and other files. On Windows, dynamic pages are also part of this cache and can be attacked as well, e.g., data, heap, and stacks. Our side channel permits unprivileged monitoring of accesses to these pages of other processes, with a spatial resolution of 4kB and a temporal resolution of 2µs on Linux (≤6.7 measurements per second), and 466ns on Windows 10 (≤223 measurements per second). We systematically analyze the side channel by demonstrating different hardware-agnostic local attacks, including a sandbox-bypassing high-speed covert channel, an ASLR break on Windows 10, and various information leakages that can be used for targeted extortion, spam campaigns, and more directly for UI redressing attacks. We also show that, as with hardware cache attacks, we can attack the generation of temporary passwords on vulnerable cryptographic implementations. Our hardware-agnostic attacks can be mitigated with our proposed security patches, but the basic side channel remains exploitable via timing measurements. We demonstrate this with a remote covert channel exfiltrating information from a colluding process through innocuous server requests.

We consider the disk cache (file-system cache) information channel, and show how it can be exploited on various systems to yield potentially sensitive information. Our approach can be used locally by an unprivileged adversary to detect whether another user is writing to disk, and if so, the rate at which data is being written. Further, we also show how an attacker can detect whether specific files have been recently accessed by the victim. We then extend this attack to remote access through a web server, using timing analysis to identify recent access of chosen pages.

While the growth of cloud-based technologies has benefited the society tremendously, it has also increased the surface area for cyber attacks. Given that cloud services are prevalent today, it is critical to devise systems that detect intrusions. One form of security breach in the cloud is when cyber-criminals compromise Virtual Machines (VMs) of unwitting users and, then, utilize user resources to run time-consuming, malicious, or illegal applications for their own benefit. This work proposes a method to detect unusual resource usage trends and alert the user and the administrator in real time. We experiment with three categories of methods: simple statistical techniques, unsupervised classification, and regression. So far, our approach successfully detects anomalous resource usage when experimenting with typical trends synthesized from published real-world web server logs and cluster traces. We observe the best results with unsupervised classification, which gives an average F1-score of 0.83 for web server logs and 0.95 for the cluster traces.