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(CLOUD 2020) Analyzing CNN Based Behavioural Malware Detection Techniques in Cloud IaaS

About This Webinar

Abstract: 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.

Authors: Andrew D McDole (Tennessee Technological University, USA); Mahmoud Abdelsalam (Manhattan College, USA); Maanak Gupta (Tennessee Technological University, USA); Sudip Mittal (University of North Carolina Wilmington, USA)

Email: amcdole42@students.tntech.edu, mahmoud.ahlym@gmail.com, mgupta@tntech.edu, mittals@uncw.edu

Who can view: Everyone
Webinar Price: Free
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My name is Andrew McDole. I am a Master's student at the Tennessee Technological University in Cookeville, Tennessee. My thesis is centered around malware detection in Cloud IaaS with deep learning. I am also an SFS scholar with the Scholarship for Service program.
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