Cybersecurity Challenges in Cloud-Edge Computing Convergence: A Systematic Analysis and Adaptive Defense Framework

Abstract

The convergence of cloud computing and edge computing has emerged as a foundational architecture for supporting latency-sensitive and data-intensive applications such as autonomous driving, smart healthcare, and industrial automation. By integrating the scalable computing resources of the cloud with the real-time processing capabilities of edge nodes, this convergence optimizes application performance while reducing bandwidth consumption. However, the distributed and heterogeneous nature of cloud-edge architectures introduces unprecedented cybersecurity challenges that cannot be adequately addressed by traditional defense mechanisms designed for centralized cloud environments. This study conducts a systematic analysis of cybersecurity vulnerabilities in cloud-edge convergence, categorizing them into edge node, communication, cloud-edge orchestration, and data lifecycle layers. Through evaluating 135 peer-reviewed studies and real-world incident data from 2023 to 2025, the research assesses the effectiveness of existing mitigation measures, including edge-native intrusion detection, secure orchestration protocols, and privacy-preserving data processing. An adaptive defense framework integrating dynamic risk assessment, multi-layered access control, and collaborative threat intelligence sharing is proposed to address the unique constraints of cloud-edge environments, such as resource heterogeneity and real-time processing requirements. The findings highlight the urgency of context-aware security solutions and cross-layer defense coordination, providing actionable insights for researchers, cloud-edge service providers, and policymakers. This study contributes to the advancement of cloud-edge security resilience by bridging the gap between theoretical research and practical implementation in distributed computing ecosystems.