Threat Modeling Based on Randomized Seeding Attacks In Cloud Virtual Machines

Abstract

Threats in virtual machines have been a major challenge in most cloud data centers. The attack/ threat begins from physical machines (hosts) and spreads to all virtual machines(guests). As a result, the virtual machines get infected rapidly by recursive growth of the seeds/ nodes generated in a random manner. This paper proposes threat modeling based on randomized growth of these seeds or nodes. The simulated attacks are free from a deterministic pattern and hence all threats can be detected and prevented. The aim of this work is to develop a mathematical model to prevent seeding attack on virtual machines on the cloud. It presents both Lucas and Fibonacci series and draw relationship between them where by each VM affected is identified and the VMs can be prevented from these attacks.