[This article belongs to Volume - 54, Issue - 02]
Gongcheng Kexue Yu Jishu/Advanced Engineering Science
Journal ID : AES-24-12-2022-590

Title : AN AUTOMATED FRAMEWORK FOR PROACTIVE AND REACTIVE CLOUD DATA CENTER SECURITY
A. Mahendar1, Dr. K. Shahu Chatrapati2

Abstract :

As the need for greater and better scalability grows, the usage of cloud computing is becoming increasingly widespread. Virtually all traditional applications are being moved to the cloud by consumers, service providers, and application owners alike. Customers as well as service providers gain from this. Customers benefit from cost reductions by achieving maximum scalability and providing additional features at the lowest possible cost, which in turn leads to higher customer satisfaction in the long run. However, these actions, as well as new installations, are drawing the attention of hackers and attackers. Recent attacks on significant services, such as search engines and storage services, as well as vital applications ranging from healthcare to defense, have all been documented and documented. The attacks may be limited to data research, data consumption, or even service destruction, or they could be more extensive. The most challenging part of detecting these attacks is figuring out what kind of connection request is being made. Defensive security is not adequate to protect cloud services; security as a service must be implemented automatically and constantly across all applications, services, and data centers to be truly safe. Several recent research have shown significant advancements in the detection of security breaches. Despite this, security breaches have occurred despite these precautions being taken. Additionally, the current techniques are not automated and thus cannot be included in the to detect any anomalies in request types, this research proposes an automated framework methodology for identifying the application traffic pattern, among other things. The primary purpose of this effort is to identify different types of attacks and prevent more cloud service damage while using little computer resources. In addition, this research identifies a preventive mechanism against typical assault types. The research also demonstrates how to use traffic pattern analysis to discover new sorts of attacks, which will help to make the cloud computing application hosting industry safer.