LOF: identifying density-based local outliers
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Intrusion Detection in Virtual Machine Environments
EUROMICRO '04 Proceedings of the 30th EUROMICRO Conference
Protecting host-based intrusion detectors through virtual machines
Computer Networks: The International Journal of Computer and Telecommunications Networking
Modeling intrusion detection system using hybrid intelligent systems
Journal of Network and Computer Applications - Special issue: Network and information security: A computational intelligence approach
Antfarm: tracking processes in a virtual machine environment
ATEC '06 Proceedings of the annual conference on USENIX '06 Annual Technical Conference
Stealthy malware detection through vmm-based "out-of-the-box" semantic view reconstruction
Proceedings of the 14th ACM conference on Computer and communications security
VMM-based hidden process detection and identification using Lycosid
Proceedings of the fourth ACM SIGPLAN/SIGOPS international conference on Virtual execution environments
VNIDA: Building an IDS Architecture Using VMM-Based Non-Intrusive Approach
WKDD '08 Proceedings of the First International Workshop on Knowledge Discovery and Data Mining
Virtual machine monitor-based lightweight intrusion detection
ACM SIGOPS Operating Systems Review
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Cloud storage solutions have increasingly gained in popularity as they offer a convenient method of maintaining one's data all in one place, with the ability to access it from anywhere at any time. In this paper, we leverage a virtualization-based intrusion detection infrastructure to build secure cloud storage systems. The intrusion detection system uses machine learning techniques applied to data available at the virtual machine monitor layer to identify the presence of malicious activity during a workload's execution. Our results show that by running a cloud storage server in a virtual execution setting, we can detect real-world malware attacks at a high detection rate of over 98%, with fewer than 3% false alarms.