Detection of Information leakage in cloud
Mansaf Alam, Shuchi Sethi

TL;DR
This paper proposes a machine learning-based framework for detecting covert information leakage channels in cloud environments, focusing on accuracy, cost-efficiency, and robustness against noisy data.
Contribution
It introduces a signature-based detection framework utilizing feature extraction and SVMs to identify covert channels in cloud traffic, addressing limitations of existing methods.
Findings
High detection accuracy achieved
Low-cost and scalable approach
Robust performance in noisy environments
Abstract
Recent research shows that colluded malware in different VMs sharing a single physical host may use a resource as a channel to leak critical information. Covert channels employ time or storage characteristics to transmit confidential information to attackers leaving no trail.These channels were not meant for communication and hence control mechanisms do not exist. This means these remain undetected by traditional security measures employed in firewalls etc in a network. The comprehensive survey to address the issue highlights that accurate methods for fast detection in cloud are very expensive in terms of storage and processing. The proposed framework builds signature by extracting features which accurately classify the regular from covert traffic in cloud and estimates difference in distribution of data under analysis by means of scores. It then adds context to the signature and…
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Taxonomy
TopicsNetwork Security and Intrusion Detection · Internet Traffic Analysis and Secure E-voting · Advanced Malware Detection Techniques
