Security And Dependability Challenges In Virtualized Networking And Computing Environments

Abstract

The Fifth Generation (5G) Of Mobile Networks Introduces Transformative Services With Strict Requirements For Performance, Security, And Dependability. Multi-access Edge Computing (MEC), A Cornerstone Of 5G, Enhances Network Efficiency By Reducing Latency, Enabling Real-time Local Awareness, Supporting Cloud Offloading, And Mitigating Traffic Congestion. These Advancements Are Critical For Mission-critical Applications But Come With Security And Reliability Challenges That Are Of- Ten Underexplored Alongside Performance. This Survey Paper Addresses This Gap, Presenting A Comprehensive Analysis Of 5G MEC’s Security, Dependability, And Performance, Along With State-of-the-art Solutions And Challenges In These Domains. Inter- Net Of Things (IoT) Security Has Garnered Significant Attention Due To Its Vulnerability To Attacks Such As Denial Of Service (DoS) And Data Breaches. A Novel Machine Learning (ML)-based Security Framework Leveraging Software Defined Networking (SDN) And Network Function Virtualization (NFV) Is Proposed To Counter These Threats. This Framework Employs AI-driven Mon- Itoring And Anomaly-based Intrusion Detection, Demonstrating High Accuracy (99.715G Also Enables Diverse Services Through Virtualization, Softwarization, And Network Slicing, Fostering The Rise Of Mobile Virtual Network Operators (MVNOs). While These Technologies Enhance Flexibility, They Also Introduce Complex Security Implications. This Paper Reviews Security Challenges And Potential Solutions For MVNOs, Emphasizing The Critical Role Of Robust Virtualization Techniques, Such As Microkernel-based Environments, Which Enhance Resource Utilization And Security. In The Domain Of Smart Grids (SG), Integrating Power Networks With Information Technologies Introduces Vulnerabilities Due To Increased Automation And Connectivity. The Application Of ML In SG Enhances Attack Detection And Threat Analysis But Also Exposes Systems To Adversarial ML Attacks. This Survey Examines The Security And Privacy Challenges Of SG, Presenting Taxonomies And Novel Findings To Address These Issues.

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