The ultimate aim of dynamic load balancing in cloud computing systems is to maximise the efficiency with which resources are utilised and workloads are distributed. Given that load balancing is a multi-objective process and that response time is a priority, the Harris hawk optimisation (HHO) algorithm was developed as a unique solution for dynamic load balancing. Based on burden distribution and resource utilisation, the HHO algorithm is responsible for dynamically assigning workloads to virtual machines (VMs). Through iterative interactions and position updates, the hawks investigate the solution space, determine the optimal method for dividing the work, and adapt to the ever-changing conditions of the workload. The HHO algorithm has been demonstrated to be effective and efficient in the management of dynamic load balancing via a series of experimental evaluations and comparisons with other load-balancing approaches. These discoveries have led to quicker response times and more efficient resource utilisation. Utilising the collaborative search behaviour of hawks, this technique provides a solution that is both practicable and effective for addressing load balancing concerns in dynamic scenarios.
Cloud computing is an attractive technology paradigm that has been widely used as a tool for storing and analyzing the data of different users. Since access to the cloud is achieved through the Internet, data stored in clouds is susceptible to attacks from external as well as internal intruders. Henceforth, cloud service providers (CSPs) need to take action in order to provide a secure framework that would detect intrusion in the cloud and protect and secure customer information against hackers and intruders. This paper proposes a Sgd-LSTM and signature-based access control policy based Intrusion Detection and Prevention System (IDPS) model which is meant to detect and prevent various intrusions in the cloud. The proposed system includes three phases: the user registration phase, intrusion detection phase, and intrusion prevention phase. Initially, user registration is performed based on a unique ID and password, and then, the password is converted into hashcode by using the C2HA algorithm and then stored in the cloud for authentication purposes. In the intrusion detection phase, the status of cloud data is predicted by employing the Sgd-LSTM classifier in order to discard the intruder data packets from the cloud. At last, in the intrusion prevention phase, data access to the cloud environment is controlled by using signature-based user authentication in order to authenticate the legitimate user. The proposed classifier can effectively detect the intruders, which was experimentally proved by comparing it with the existing classifiers.
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