Abstract

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

Highlights

  • There is an emerging model in computing called cloud computing which provides users with unlimited services

  • The number of cloud users is considered from the range of 1000 to 5000 which is taken as input while conducting the experiments

  • The User Allocation Rate (UAR) efficiency is measured as the ratio of the number of the authorized user is identified through the authentication to the total number of cloud users in the cloud environment

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Summary

Introduction

There is an emerging model in computing called cloud computing which provides users with unlimited services. There is a necessity to verify whether the user is authenticated or not and increase the data security for achieving secured load-balanced data storage with minimized space consumption on the cloud. To address the issues related to intrusion (attack) discovery, authentication and load balancing on the cloud, the proposed Authentication based User-Allocation with Merkle based Hashing-Tree (AUA-MHT) technique is employed. In this proposed technique, two important stages such as Identity-based authentication and Merkle Hash Tree construction are carried out to attain secured cloud data storage with less space complexity. The rest of the paper is organized as follows: Section 2 details the related works of IDS and load balancing in the Cloud Server, Section 3 briefs about the Existing System and Methodologies of the proposed systems, Section 4 provides the Results and Discussion of the proposed research work and Section 5 concludes the article

2.Related Work
4.Results and Discussions
Performance Analysis of Space Complexity
5.Conclusion

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