Conference Paper - 2019
MASCOTS 2019 - 27th IEEE International Symposium on the Modeling, Analysis, and Simulation of Computer and Telecommunication Systems
Companies are more and more inclined to use collaborative cloud resources when their maximum internal capacities are reached in order to minimize their TCO. The downside of using such a collaborative cloud, made of private clouds' unused resources, is that malicious resource providers may sabotage the correct execution of third-party-owned applications due to its uncontrolled nature. In this paper, we propose an approach that allows sabotage detection in a trustless environment. To do so, we designed a mechanism that (1) builds an application fingerprint considering a large set of resources usage (such as CPU, I/O, memory) in a trusted environment using random forest algorithm, and (2) an online remote fingerprint recognizer that monitors application execution and that makes it possible to detect unexpected application behavior. Our approach has been tested by building the fingerprint of 5 applications on trusted machines. When running these applications on untrusted machines (with either homogeneous, heterogeneous or unspecified hardware from the one that was used to build the model), the fingerprint recognizer was able to ascertain whether the execution of the application is correct or not with a median accuracy of about 98% for heterogeneous hardware and about 40% for the unspecified one.