Developing Hybrid Privacy Channels in Cloud Architecture using Several Agents

An information security plan often assesses the risk, the likelihood that it would materialize as a result of an unusual incident, and the related ramifications for cloud enterprises. Different computer safety and risk mitigation techniques are implemented by different cloud organizations and disciplines. When comparing the risk management techniques that cloud firms now use, it becomes clear that they have shortcomings since they only take into account traditional criteria and ignore an agent-based approach that takes into account every aspect of the risk involved. The failure to include well-known risk mitigation frameworks, appropriate comparisons, and agent procedures is what spurred this investigation. This paper presents the Agent-Based Security of Knowledge Model for Blended Cloud Computing, a complete methodology that combines cloud-specific methodologies for analyzing and comparing well-known systems now in use, to solve challenges in cloud computing risk. New elements are also included from the assessed techniques. The goal of introducing intelligent agents and software agents is to provide a decision-making framework that will direct organizational actions against danger agents by using data from security agents. These agents are in charge of gathering trustworthy data and organizing it for frameworks. The research focuses on risk assessment methods that compute results by examining resources, possible threats, weaknesses, and related actions. ISO/IEC 27005:2011 is the best technique inside the Agent-Based security professionals Framework (ABISF) after a comprehensive comparison with popular Individual Security Assessment of Risk (ISRA) methodologies. The suggested framework is developed using a fuzzy inference method based on fuzzy set theory, and its effectiveness is verified using fuzzy logic rules in MATLAB®. The inconclusive results confirm that the proposed technique has potential to protect privacy of information in cloud settings.

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