  Cloud Infrastructure as a Service (IaaS) is vulnerable to malware due to its
exposure to external adversaries, making it a lucrative attack vector for
malicious actors. A datacenter infected with malware can cause data loss and/or
major disruptions to service for its users. This paper analyzes and compares
various Convolutional Neural Networks (CNNs) for online detection of malware in
cloud IaaS. The detection is performed based on behavioural data using process
level performance metrics including cpu usage, memory usage, disk usage etc. We
have used the state of the art DenseNets and ResNets in effectively detecting
malware in online cloud system. CNN are designed to extract features from data
gathered from a live malware running on a real cloud environment. Experiments
are performed on OpenStack (a cloud IaaS software) testbed designed to
replicate a typical 3-tier web architecture. Comparative analysis is performed
for different metrics for different CNN models used in this research.
