IHIDS: Introspection-based hybrid intrusion detection system in cloud environment

Kashyap, Amita; Kumar, Gautam; Jangir, Sunita; Pilli, Emmanuel S.; Mishra, Preeti

doi:10.1109/icacci.2017.8125921

Cited by 4 publications

(1 citation statement)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Compared to previous work [32], our design for this feature is more than a simple collection of various information from different layers. We combine all the knowledge of system state and integrate them into a new dataset with information correlation.…”

Section: Multilayer Visibilitymentioning

confidence: 99%

Tenant-Oriented Monitoring for Customized Security Services in the Cloud

Zhou

Wang

et al. 2019

Symmetry

View full text Add to dashboard Cite

The dramatic proliferation of cloud computing makes it an attractive target for malicious attacks. Increasing solutions resort to virtual machine introspection (VMI) to deal with security issues in the cloud environment. However, the existing works are not feasible to support tenants to customize individual security services based on their security requirements flexibly. Additionally, adoption of VMI-based security solutions makes tenants at the risk of exposing sensitive information to attackers. To alleviate the security and privacy anxieties of tenants, we present SECLOUD, a framework for monitoring VMs in the cloud for security analysis in this paper. By extending VMI techniques, SECLOUD provides remote tenants or their authorized security service providers with flexible interfaces for monitoring runtime information of guest virtual machines (VMs) in a non-intrusive manner. The proposed framework enhances effectiveness of monitoring by taking advantages of architectural symmetry of cloud environment. Moreover, we harden our framework with a privacy-preserving capacity for tenants. The flexibility and effectiveness of SECLOUD is demonstrated through a prototype implementation based on Xen hypervisor, which results in acceptable performance overhead.

show abstract

Section: Multilayer Visibilitymentioning

confidence: 99%