Seeking Black Lining In Cloud

TL;DR

This paper presents a detection framework for identifying time-synchronized confidentiality attacks in cloud environments, utilizing data analysis and binary classification without assuming specific data distributions.

Contribution

It introduces a novel, distribution-agnostic detection framework for covert channel attacks in cloud security based on features derived from Google cluster trace data.

Findings

Effective detection of time-synchronized attacks

Framework generalizes to other system flow and fault detection

No assumptions on data distribution required

Abstract

This work is focused on attacks on confidentiality that require time synchronization. This manuscript proposes a detection framework for covert channel perspective in cloud security. This problem is interpreted as a binary classification problem and the algorithm proposed is based on certain features that emerged after data analysis of Google cluster trace that forms base for analyzing attack free data. This approach can be generalized to study the flow of other systems and fault detection. The detection framework proposed does not make assumptions pertaining to data distribution as a whole making it suitable to meet cloud dynamism.