Published Paper Details:

The need for secure communication and data protection has become increasingly important in the digital age. As the use of digital technologies continues to grow, the need for secure communication and data protection also increases. Anomaly detection is a crucial aspect of data analytics that can identify suspicious behavior and detect malicious activities. This is particularly crucial in the cloud computing environment, where data is stored on multiple servers and accessed remotely by various users. The Local Pollination Grey Wolf Optimizer (LPGWO) is a global optimization algorithm that has been utilized in various applications. It is based on the concept of "cognitively guided exploration," which is a form of local search that utilizes an individual's experiences to direct the exploration. It has been successfully used to solve optimization problems in various fields, such as image processing, communication networks, and cryptography. The proposed algorithm is evaluated using various parameters, including the number of iterations, time complexity, and success rate. The performance of the proposed algorithm is then compared to existing RSA algorithms to determine its superiority. This paper presents a Local Pollination Grey Wolf Optimizer (LPGWO)-based RSA algorithm for anomaly detection in heterogeneous cloud data using machine learning techniques.