Abstract: Data security and privacy are perennial concerns related to cloud migration whether it is about applications, business or customers. The multi-tenant environment is vulnerable to several types of attacks that hackers aiming towards the sensitive data in the broad access area of cloud. Intrusion Detection and Protection Systems (IDPS) are a significant part of the security framework from the beginning of cloud usage. Since, the security framework itself being the primary target of attackers an unbreakable strategy is needed for its protection. In this study, a novel security architecture for cloud environment designed with IDPS components as a graded multitier defense framework. The proposed model is a defense formation of collaborative IDPS components with dynamically revolving alert data placed in multiple tiers of Virtual Local Area Networks (VLANs). Even if many techniques existing for securing the cloud with IDPS, the alert generation delays prevalent due to the static correlation and aggregation. The novel security architecture proposed with two contributions for impregnable protection, one is to reduce alert generation delay by dynamic correlation and the second is to support the supervised learning of malware detection through, system call analysis. The defense formation facilitates malware detection with linear Support Vector Machine-Stochastic Gradient Descent (SVM-SGD) statistical algorithm. The proposed model of Dynamic Correlated, Graded IDPS (DCGIDPS) for cloud requires little computational effort to counter the distributed, coordinated attacks efficiently.