APPLICATION OF PSEUDORANDOM SEQUENCE GENERATORS FOR MODELING CYBERSECURITY EVENTS IN ANOMALY DETECTION AND INTRUSION DETECTION SYSTEMS
DOI:
https://doi.org/10.28925/2663-4023.2026.35.1460Keywords:
cybersecurity, anomaly detection, intrusion detection, IDS/IPS, pseudorandom sequence generator, synthetic data, Poisson process, statistical modeling.Abstract
The paper proposes an approach to generating synthetic streams of cybersecurity events using pseudorandom sequence generators (PRSGs) and statistical modeling for anomaly and intrusion detection in IDS/IPS systems. A mathematical model is developed in which a pseudorandom sequence is used to generate inter-event intervals through an inverse exponential transformation, while event timestamps are obtained by cumulatively summing these intervals. Each event is characterized by a category and a vector of attributes. The generation parameters are determined by the activity regime, which may be normal, elevated, or anomalous. Anomalies are treated as multidimensional deviations that may manifest as changes in event stream intensity, the categorical structure of events, or the statistical characteristics of their parameters. The applicability limits of the homogeneous Poisson process approximation for modeling complex streams of cybersecurity events are analyzed. The proposed model is considered in three scenarios: generating training and test datasets for IDS/IPS, modeling events for honeypot systems, and simulating scanning activity events. A methodology for evaluating the statistical properties and suitability of the generated event streams is formulated. The results are theoretical and methodological in nature; empirical validation of the model using real-world datasets is identified as a direction for future research.
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