Design and Development of Track as a Service (TAAS) Model to Track and Optimize the Threats in Personal Cloud Computing Using Levy Flight SVM

Abstract

The exponential growth in the use of personal cloud services, security has become an extremely important concern. The fact that existing frameworks do not satisfy the requisite standards of precision and adaptability is frequently the impetus behind the need for a solution that is tailored to the specific needs of the situation. A significant knowledge gap exists because of the fact that the personal cloud security frameworks that are now in place are not adaptable enough to deal with threats as they evolve. Their deficiencies stem from the fact that they do not perform sufficient monitoring and enhancement of security measures in a dynamic manner. This research takes use of a customised architecture that leverages machine learning techniques, specifically the Levy flying support vector machine (SVM), to compensate for this shortcoming. The unique approach that we have developed, which combines machine learning with a dynamic TaaS model, is something that we think will contribute to the closing of this knowledge gap. A fundamental component of the proposed strategy is the utilisation of machine learning techniques, specifically Levy fly support vector machines (SVMs), with the objective of accurately tracking threats. Optimising security can be accomplished through the TaaS paradigm, which offers an approach that is both flexible and dynamic. The ongoing learning and adaption of the framework works towards the goal of staying one step ahead of new hazards as they emerge. One of the results is an improved capability to both monitor and enhance the level of security that cloud computing provides for individual users. Following the implementation of the proposed architecture, users can anticipate improved reaction times, higher security efficacy, and faster threat detection.