Data mining based CIDS: Cloud intrusion detection system for masquerade attacks [DCIDSM]

Abstract

Data mining has been gaining popularity in knowledge discovery field. In recent years, data mining based intrusion detection systems (IDSs) have demonstrated high accuracy, good generalization to novel types of intrusion, and robust behavior in a changing environment. Still, significant challenges exist in design and implementation of production quality IDSs. Masquerade attacks pose a serious threat for cloud system due to the massive amount of resource of these systems. This paper presents a Cloud Intrusion Detection System (CIDS) for CIDD dataset, which contains the complete audit parameters that help in detecting more than hundred instances of attacks and masquerades that exist in CIDD. It also offers numerous advantages in terms of alert infrastructure, security, scalability, reliability and also has data analysis tools.