Cyber Resilience Model Based on a Self Supervised Anomaly Detection Approach

Cyber resilience plays an important role in dealing with cybersecurity and business continuity uncertainty in the post-COVID-19 era. The fundamental problem of cyber resilience is the complexity of real-world problems. Therefore, it is necessary to reduce the complexity of real-world problems to be simple and easy to analyze through cyber resilience model. The first part is the representational model by utilizes world models. It utilizes the stochastic nature of latent data to generate log-likelihood values by data-generating process. The second part is the inference model. This concludes the observation of log-likelihoods using a self-supervised anomaly detection approach. This is related to optimizing decision boundary in anomaly detection, which is achieved by supervising two competing hypotheses based on bias-variance alignment and likelihood ratios. The optimization operates a dynamic threshold supervised by a supervisory signal from the underlying structure of log-likelihoods. The paper contributes by conducting research on the cyber resilience model from the perspective of statistical machine learning. It enhances the representational modeling of world models with the Gaussian mixture model for multimodal regression (GMMR). Additionally, it examines the issue of misleading log-likelihood for out-of-distribution inputs caused by the generalization error and optimizes decision boundary in minimizing the generalization error with a new metric named the harmonic likelihood ratio (HLR). Finally, it aims to boost the performance of anomaly detection using self-supervised learning.

@article{Cahyono2024,
  title     = {Cyber Resilience Model Based on a Self Supervised Anomaly Detection Approach},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {11},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Eko Budi Cahyono and Suriani Binti Mohd Sam and Noor Hafizah Binti Hassan and Amrul Faruq},
  doi       = {10.14569/IJACSA.2024.0151153},
  url       = {https://doi.org/10.14569/IJACSA.2024.0151153}
}