Efficient, adversarial neighbor discovery using logical channels on Microsoft Azure

Abstract

We introduce an effective technique that exploits logical channels for malicious co-location and target identification on Microsoft Azure cloud instances. Specifically, we employ-two co-location scenarios: targeted co-location with a specific victim or co-location with subsequent identification of victims of interest. We develop a novel, noise-resistant co-location detection method through the network channel that provides fast, reliable results with no cooperation from the victim. Also, our method does not require access to the victim instance neither as a legitimate user nor a malicious attacker. The efficacy of the proposed technique enables practical QoS degradation attacks which are easy and cheap to implement yet hard to discover. The slightest performance degradation in web interfaces or time critical applications can result in significant financial losses. To this end, we show that once co-located, a malicious instance can use memory bus locking to render the victim server unusable to the customers. This work underlines the need for cloud service providers to apply stronger isolation techniques.