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Security and Communication Networks
Volume 2018, Article ID 4216240, 18 pages
https://doi.org/10.1155/2018/4216240
Research Article

Leveraging KVM Events to Detect Cache-Based Side Channel Attacks in a Virtualization Environment

Laboratory for Cyber Resilience, Nara Institute of Science and Technology, 8916-5 Takayama, Ikoma, Nara 630-0192, Japan

Correspondence should be addressed to Ady Wahyudi Paundu; pj.tsian.si@9ka.udnuap.yda

Received 25 September 2017; Revised 13 December 2017; Accepted 23 January 2018; Published 25 February 2018

Academic Editor: Wojciech Mazurczyk

Copyright © 2018 Ady Wahyudi Paundu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Cache-based side channel attack (CSCa) techniques in virtualization systems are becoming more advanced, while defense methods against them are still perceived as nonpractical. The most recent CSCa variant called Flush + Flush has showed that the current detection methods can be easily bypassed. Within this work, we introduce a novel monitoring approach to detect CSCa operations inside a virtualization environment. We utilize the Kernel Virtual Machine (KVM) event data in the kernel and process this data using a machine learning technique to identify any CSCa operation in the guest Virtual Machine (VM). We evaluate our approach using Receiver Operating Characteristic (ROC) diagram of multiple attack and benign operation scenarios. Our method successfully separate the CSCa datasets from the non-CSCa datasets, on both trained and nontrained data scenarios. The successful classification also include the Flush + Flush attack scenario. We are also able to explain the classification results by extracting the set of most important features that separate both classes using their Fisher scores and show that our monitoring approach can work to detect CSCa in general. Finally, we evaluate the overhead impact of our CSCa monitoring method and show that it has a negligible computation overhead on the host and the guest VM.