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Abstract Security is one of the major challenges in Cloud Computing. This thesis presents an Online Smart Disguise Framework (OSDF) for cloud computing security. The OSDF employs dynamic, proactive, and real-time Moving-Target Defense (MTD) against cloud computing attacks. The OSDF relies on two main pillars. The first pillar is a behavior-obscuring module that frequently live-migrates Virtual Machines (VMs) between heterogeneously configured computing nodes to avoid co-residency and virtualization based attacks. The second pillar limits attack dispersion between same-host VMs by migrating maliciously behaving VMs to remote isolated compute node acting as a quarantine zone. This pillar is guided by a smart Intrusion Detection System (IDS) to monitor the VM’s system calls that searching for suspicious activities. To evaluate the proposed OSDF’s efficiency and effectiveness on limiting attack dispersion, we devised Vulnerable Exposed Attacked Recovered (VEAR) mathematical model. The VEAR model inspired by an epidemiological model commonly used to investigate disease dispersion on cooperative communities called Susceptible Exposed Infected Recovered (SEIR) epidemic model. The implementation of the OSDF is conducted on OpenStack private cloud. The Simulation results show the effectiveness of OSDF MTD approach in decreasing the number of attacked VMs even for fast-spreading worms. Furthermore,both iii the NAS Parallel Benchmark (NPB) and the SysBench are used to evaluate OSDF’s efficiency for cloud-hosted VMs running both statefull and stateless applications. |