Hypervisor Attack Detection Using Advanced Encryption Standard (HADAES) Algorithm on Cloud Data

Abstract

– Cloud computing demonstrates excellent power to yield cost-efficient, easily manageable, flexible, and charged resources whenever required, over the Internet. Cloud computing, can make the potential of the hardware resources to increase huge through best and shared usage. The growth of the cloud computing concept has also resulted in security challenges, considering that there are resource sharing, and it is moderated with the help of a Hypervisor which can be the target of malicious guest Virtual Machines (VM) and remote intruders. The hypervisor itself is attacked by hackers. Since the hypervisor is attacked, the VMs under the hypervisor is also attacked by the attackers. Hence, to prevent the problems stated above, in this study, Enhanced Particle Swarm Optimization (EPSO) with Hypervisor Attack Detection using Advanced Encryption Standard (HADAES) algorithm is introduced with the intent of improving the cloud performance on the whole. This work contains important phases such as system model, optimal resource allocation, and hypervisor attack detection. The system model contains the data center model, migration request model, and energy model over the cloud computing environment. Resource allocation is done by using the EPSO algorithm which is used to select the optimal resources using local and global best values. Hypervisor attack detection is done by using HADAES algorithm. It is helpful to determine the hypervisor and VM attackers also it is focused to provide higher security for cloud data. From the test result, it is concluded that the proposed algorithm yields superior performance concerning improved reliability, throughput, and reduced energy consumption, cost complexity, and time complexity than the existing methods. The effectiveness of IDS depends on its capacity to strike a balance between the number of defenses and the number of false positives or detecting errors. algorithm. The best and the mean costs of the population members and the execution time when applying the EPSO method. In this study, an innovative time-adaptive PSO is proposed based on the movement patterns named the movement pattern adaptation PSO (EPSO).