Intrusion Optimal Path Attack detection using ACO for Cloud Computing

Abstract

As the cloud infrastructure is simultaneously shared by millions of consumers, heinous use of cloud resources are also increasing. It makes ways to attackers to set up attacks by exploiting the vulnerabilities. And obviously, these attacks are leading to severe disasters as innocent consumers are unknowingly sharing cloud resources with harmful attackers. To prevent the occurrence of cloud attacks, attack graph based framework is proposed in this paper. Here, an attack path sketches an attack scenario by a streak of threats ranging in severity rating that shows how popular a particular cloud network service is in comparison. In a dynamic cloud environment, the proposed framework can disclose an optimal attack path thereby preventing cloud attacks. In cloud system the infrastructure is shared by potentially millions of users, which benefits the attackers to exploit vulnerabilities of the cloud. An instrument for analyzing multi-stage, multi-host assault scenarios in networks is the attack graph. It might not be possible for the administrator to patch every vulnerability n a large number of assault paths in an attack graph. The administrator might not be able to fix every vulnerability. To identify the most preferred or ideal assault path from a particular attack graph in a setting Ant Colony Optimization (ACO) algorithm is used.