Reinforcement Learning Driven Self-Adaptation in Hypervisor-Based Cloud Intrusion Detection Systems (RLDAC-IDS)

With the rise in cloud adoption, securing dynamic virtual environments remains a significant challenge. While traditional Intrusion Detection Systems (IDS) have attempted to address security concerns in the cloud mostly through static detection rules and without adaptation capabilities to identify new attack vectors, a self-optimizing framework called Reinforcement Learning-Driven Self-Adaptation in Hypervisor-Based Cloud Intrusion Detection Systems (RLDAC-IDS) is suggested to overcome this limitation. RLDAC-IDS leverages the inherent visibility of hypervisors into virtualized resources to gain valuable insights into cloud operations and threats. Its key components include real-time behavioral analysis, anomaly detection, and identification of known threats. The innovation of RLDAC-IDS lies in the incorporation of reinforcement learning to continuously improve the detection rules and responses. RLDAC-IDS exemplifies intelligent intrusion detection through its ability to learn and adapt to new threat patterns autonomously. By continuous optimization and intelligent intrusion detection techniques, the system progresses to tackle emerging attack vectors while minimizing false alarms. In contrast, RLDAC-IDS is highly adaptive and can easily adjust to the changing conditions of cloud environments. In summary, RLDAC-IDS represents a major advancement in cloud IDS through its adaptive, self-learning approach, overcoming the limitations of existing solutions to provide robust protection amidst the complexities and dynamics of modern virtualized settings.

@article{Qaffas2024,
  title     = {Reinforcement Learning Driven Self-Adaptation in Hypervisor-Based Cloud Intrusion Detection Systems (RLDAC-IDS)},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {7},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Alaa A. Qaffas},
  doi       = {10.14569/IJACSA.2024.0150745},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150745}
}