Adaptive Hybrid Intrusion Detection for Realistic Zero-Day Attacks in Cloud and Edge Environments

New and unknown attack patterns are creating more cybersecurity issues for cloud environments. Intrusion detection systems (IDS) are usually capable of high-performance in closed-world scenarios and are less effective in realistic zero-day scenarios. This work presents an adaptive hybrid intrusion detection mechanism for cloud environments based on the combination of Random Forest, XGBoost and AutoEncoder models, under a single decision-making framework. A fully reproducible pipeline was built directly from the raw data of CICIDS2017, using consistent preprocessing and feature engineering. In contrast with traditional IDS researches that are evaluated with standard train-test evaluation process, realistic zero-day assessment was carried out by conducting attack-family holdout experiments, including WebAttack, DDoS, and Infiltration scenarios. The results showed that both attack families and adaptive decision strategies were different, with WebAttack using XGBoost-dominant behavior and Infiltration using anomaly-focused behavior for an 85.30% and 88.89% recall, respectively. The proposed framework was also implemented on edge hardware (Raspberry Pi) and has been shown to have low-inference latency and only moderate resource usage. The results reveal that adaptive hybrid decision behavior leads to a more robust decision behavior under realistic zero-day conditions, while having a feasible deployment in cloud-edge environments.

@article{U2026,
  title     = {Adaptive Hybrid Intrusion Detection for Realistic Zero-Day Attacks in Cloud and Edge Environments},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {17},
  number    = {6},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {Nithin U and Ganeshayya Shidaganti and Sangeetha V and Vishwachetan D},
  doi       = {10.14569/IJACSA.2026.0170680},
  url       = {https://doi.org/10.14569/IJACSA.2026.0170680}
}