An Algorithmic Model Based on Optimization of the Production Rules for Phishing Attacks

Phishing cyber-hazards, which are a correct cyber threat to audit, monitoring, control, and data acquisition systems in the digitalization environment, are aimed at misleading participants in a complex system and editing personal digitalization data through unauthorized access. In the research work, functioning tables, production rules, algorithmic and mathematical modeling apparatus are used as a foundation for formulating, analyzing, and synthesizing the discrete adaptive behavior of large systems. In the scientific practical research work, phishing identification technologies based on production rules are implemented in solving a scientific problem by integrating access to digitalization resources into control and/or management operations and/or processes. In this research, a set of production rules is created to identify malicious and legitimate resources from URLs, URL features are extracted from a dataset of trusted platforms based on algorithmization, and logical rules are generated from these features; the authenticity of the URLs is then verified using this rule set. The results are compared with other existing models and algorithms, and two different approaches to generating production rules are developed. The study also develops a logical model for building a knowledge base from URL features and demonstrates the representation of malicious attacks through logical implications, conjunctions, and disjunctions. Finally, it tests optimized expressions based on monotone Boolean functions and their perfect disjunctive normal form (CDNF) on an independent test dataset in order to select the most efficient rule system.

@article{Kabulov2026,
  title     = {An Algorithmic Model Based on Optimization of the Production Rules for Phishing Attacks},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {17},
  number    = {3},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {Anvar Kabulov and Erkin Urinbaev and Inomjon Yarashov and Alisher Otakhonov},
  doi       = {10.14569/IJACSA.2026.0170391},
  url       = {https://doi.org/10.14569/IJACSA.2026.0170391}
}