Cloud-Native Cyberattack Reconstruction Through Deep Learning and Threat Intelligence in Distributed Enterprise Environments
DOI:
https://doi.org/10.66592/w8g06313Keywords:
Cloud-native security; Cyberat-tack reconstruction; Deep learn-ing; Threat intelligence; Distribut-ed enterprise environments; Cy-bersecurity analytics; Attack graphs; Digital forensics; Behav-ioral analysis; Intrusion detection.Abstract
The increasing adoption of cloud-native technologies, distributed applications, containerized workloads, and microservices has significantly expanded the cybersecurity challenges faced by modern enterprises. Sophisticated cyberattacks frequently exploit interconnected cloud resources, identity management systems, application programming interfaces, and distributed network infrastructures, making accurate attack reconstruction difficult using conventional security mechanisms. This research proposes an intelligent cloud-native cyberattack reconstruction framework integrating deep learning, threat intelligence, behavioral analytics, and temporal event correlation to investigate complex security incidents across distributed enterprise environments. The proposed framework collects heterogeneous security telemetry from cloud platforms, container orchestration systems, application services, network monitoring tools, and identity management infrastructures. Deep learning models analyze security events, identify suspicious behavioral patterns, and establish relationships between seemingly independent attack activities. Threat intelligence integration enriches investigation processes through known indicators of compromise, adversarial tactics, techniques, and procedures. Furthermore, graph-based attack modeling reconstructs potential attack paths, identifies compromised resources, and determines the progression of malicious activities across interconnected cloud services. Temporal correlation mechanisms establish chronological relationships between security events, while explainable artificial intelligence provides transparent investigation insights. The proposed framework aims to improve cyberattack identification, forensic investigation accuracy, threat correlation, and incident response efficiency. This research establishes an intelligent analytical approach for strengthening cybersecurity investigation and proactive threat management in distributed cloud-native enterprise environments.References
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