A Randomized Generic Lucas Seed Algorithm (RGLSA) with Tail Boosting for Threat Modeling in Virtual Machines

This research paper will analyze security threats and proposes the self-propagating model of seeding attacks in cloud computing. Threat modeling on distributed and self-organizing systems is a very important modeling paradigm which discusses and analyzes the different ways malware may propagate in such systems. The paper introduces Randomized Generic Lucas Seed Algorithm (RGLSA) with TailBoosting which is based on Lucas and Fibonacci sequences. This is a model where the Virtual machines (hosts) could get infected rapidly by collaborative and recursive growth of the seeds generated in a random order. Tail boosting is introduced, for the first time so that in the scenario of the Cloud environment getting scaled up, the attack probabilities on VM’s don’t decrease drastically. The randomized growth of the seeds ensure that simulated attacks are free from a deterministic pattern and therefore, all the more challenging to be detected.