Security Risk-Aware Resource Management Mechanism for the 5G Cloudified Infrastructure

5G wireless networks will play a critical role in society by providing ubiquitous access to end users, supporting mission critical applications, and interconnecting other critical infrastructure systems. Given its criticality, cybersecurity should be taken by design to ensure the security of running services and compliance with Service Level Agreements (SLA). This Thesis addresses the problem of security-aware resource allocation of the 5G cloudfied infrastructure aiming at minimizing the security risks while maximizing the dependability. To this end, a risk-aware edge-cloud mechanism in addition to three stochastic optimal controllers are proposed to efficiently manage the cloudified infrastructure to ensure the protection of services and applications as well as the protection of the infrastructure against cyber threats such as resource exhaustion due to flash network traffic, DoS attack, and SLA violations. To ensure a secure and SLA compliant operation of the cloudified 5G infrastructure, new and innovative cost structures are proposed to guide the optimal controllers towards a safe and sustainable operation where it is shown that they offset the cost of operationalizing the cloudified infrastructure by offering economical benefits related to the provisioning of secure and reliable services. Furthermore, we propose new security and dependability metrics that allows for the quantification of the perceived security risk of services, the probability of SLA violation, the probability of edge placement, the probability of cloud placement and the probability of a DoS attack. Considering the risk-awareness of the proposed mechanisms, they can be successfully integrated into the proposed agile security framework, which is based on an offensive posture that allows for a rapid response to cyber threats that might endanger the operations of the cloudified 5G infrastructure. Last but not least, this Thesis emphasizes the application of stochastic process and stochastic optimization which are essential tools to capture the uncertainties in dealing with the security aspects of the massive infrastructure, the demand and workload characterization.