AI-Driven Zero-Trust Security Framework for Detecting Advanced Persistent Threats in Cloud Environments

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@article{1711893,
      author = {Aidar Imashev},
      title = {AI-Driven Zero-Trust Security Framework for Detecting Advanced Persistent Threats in Cloud Environments},
      journal = {Iconic Research And Engineering Journals},
      year = {2025},
      volume = {9},
      number = {5},
      pages = {477-491},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1711893.pdf},
      abstract = {The growing complexity of Advanced Persistent Threats (APTs) poses a serious concern for cloud systems that rely on distributed resources and virtualized services. Conventional perimeter-based defenses and rule-based mechanisms are not always effective at identifying stealthy APT campaigns that dynamically evolve in multi-tenancy environments. This paper presents a Zero-Trust Security Framework based on AI that combines hybrid machine learning models with adaptive policy automation to identify, respond to, and mitigate APTs in multi-cloud ecosystems. The framework is based on the Extreme Gradient Boosting (XGBoost) to analyze structured log and network data, and a Deep Neural Network (DNN) to identify behavioral and temporal abnormalities that can be interpreted as the presence of malicious persistence or lateral movement. Based on the resulting model, trust scores are continually recalculated and dynamic access controls are imposed, consistent with Zero-Trust principles. One case study run on AWS and Azure infrastructure tests performance metrics, including detection accuracy, latency, and false-positive rate, under simulated attacks. The experimental findings suggest that the proposed framework can dramatically improve anomaly detection performance and response time compared to traditional models. Also, automated trust recalibration and microsegmentation enhance the system's overall resilience and compliance. This study demonstrates the potential of integrating artificial intelligence into Zero-Trust architectures to proactively detect and prevent APTs, providing a scalable, intelligent approach to securing cloud-native infrastructure.},
      keywords = {Artificial Intelligence (AI); Zero-Trust Architecture (ZTA); Advanced Persistent Threats (APTs); Cloud Security; Machine Learning; Behavioral Analytics; XGBoost; Deep Neural Networks; Adaptive Access Control; Policy Automation},
      month = {November},
  }