Multiple corporations and people frequently launching their data in the cloud environment. With the huge growth of data mining and the cloud storage paradigm without checking protection policies and procedures that can pose a great risk to their sector. The data backup in the cloud storage would not only be problematic for the cloud user but also the Cloud Service Provider (CSP). The unencrypted handling of confidential data is likely to make access simpler for unauthorized individuals and also by the CSP. Normal encryption algorithms need more primitive computing, space and costs for storage. It is also of utmost importance to secure cloud data with limited measurement and storage capacity. Till now, different methods and frameworks to maintain a degree of protection that meets the requirements of modern life have been created. Within those systems, Intrusion Detection Systems (IDS) appear to find suspicious actions or events which are vulnerable to a system's proper activity. Today, because of the intermittent rise in network traffic, the IDS face problems for detecting attacks in broad streams of links. In existing the Two-Stage Ensemble Classifier for IDS (TSE-IDS) had been implemented. For detecting trends on big data, the irrelevant data characteristics appear to decrease both the velocity of attack detection and accuracy. The computing resource available for training and testing of the IDS models is also increased. We have put forward a novel strategy in this research paper to the above issues to improve the balance of the server load effectively with protected user allocation to a server, and thereby minimize resource complexity on the cloud data storage device, by integrating the Authentication based User-Allocation with Merkle based Hashing-Tree (AUA-MHT) technique. Through this, the authentication attack and flood attack are detected and restrict unauthorized users. By this proposed model the cloud server verifies, by resolving such attacks, that only approved users are accessing the cloud info. The proposed framework AUA-MHT performs better than the existing model TSE-IDS for parameters such as User Allocation Rate, Intrusion Detection Rate and Space Complexity.