Insurance is an extremely diversified from one clear-cut policy to another depending on a rising demand. Internet based businesses are a booming industry and falls under categories of small, medium and enterprise. It is no exception that cybersecurity risks are exponentially presents as a continues fears in this business environment. To ensure this insecurity is handled effectively, cyber insurance policies introduced by insurance companies. However, the nature of cyber risks must be addressed in much more robust and complex algorithms. Autonomic computing with the combination of Fuzzy and Q-Learning is introduced to ensure active policies are ready to handle the uncertainty and together with the ability to learn and mitigate the unrest situation.

Insurance is an extremely diversified from one clear-cut policy to another depending on a rising demand. Internet based businesses are a booming industry and falls under categories of small, medium and enterprise. It is no exception that cybersecurity risks are exponentially presents as a continues fears in this business environment. To ensure this insecurity is handled effectively, cyber insurance policies introduced by insurance companies. However, the nature of cyber risks must be addressed in much more robust and complex algorithms. Autonomic computing with the combination of Fuzzy and Q-Learning is introduced to ensure active policies are ready to handle the uncertainty and together with the ability to learn and mitigate the unrest situation.

A Service Level Agreement (SLA) is the legal catalyst to monitor any contract violation between end users and ISPs and is embedded within a Quality of Service (QoS) framework. The key to the proposed architecture is the utilization of self-capabilities designed to have self-management over uncertainties and the provision of self-adaptive interactions. Thus, the Monitor, Analyse, Plan, Execute and Knowledge Base (MAPE-K) approach can deal with this problem together with the integration of Fuzzy and Q-Learning algorithms. The proposed experiment is in the context of autonomic computing. An adaptation manager is the main proposed component to update admission control on the ISP current resources and the ability to manage SLAs.The proposed solution, demonstrating Q-Learning works adaptive with QoS parameters, e.g. Latency, Availability and Packet Loss. With the combination of fuzzy and Q-Learning, we demonstrate that the proposed adaptation manager is able to handle the uncertainties and learning abilities. Q-Learning is able to identify the initial state from various ISPs iterations and update them with appropriate actions, reflecting the reward configurations. The higher the iterations process the higher is the increase the learning ability, rewards and exploration probability.

Autonomic management within autonomic computing framework is considered as the future and viable solution for many appliances, either in software or hardware. Nevertheless, its current research application in computer networks is mainly visible in the intra domain space, and less attention is given to inter domain between one core network and another. This paper reviews some of the work on autonomic management and presents a framework that can be extended to a global and universal solution, such as fulfilling demand on bandwidth management, Quality of Service (QOS), and Service Level Agreements (SLA). The autonomic computing self-* features are considered to show the viability of the proposed framework.