Метод виявлення вторгнень у безпроводових сенсорних мережах на основі правил специфікації та штучної нейронної мережі

S. Artіukh

doi:10.36910/6775-2524-0560-2025-60-37

S. Artіukh http://orcid.org/0000-0003-2142-1552

DOI: https://doi.org/10.36910/6775-2524-0560-2025-60-37

Keywords: wireless sensor networks, intrusion detection, specification rules, artificial intelligence, neural network, machine learning, game theory, metrics, Nash equilibrium, database, cybersecurity

Abstract

A probabilistic (stochastic) monitoring strategy based on Nash's Bayesian equilibrium was used, which allowed BB agents to automatically optimize their behavior depending on the current threat level and their own resources, as well as dynamically change their strategies, ensuring the flexibility and adaptability of the system to changes in the behavior of malicious nodes. Specification rules have been developed based on a combination of sensor node indicators that affect the quality of information transmission. The status of nodes is tracked by intrusion detection agents that passively monitor the network. A multilayer neural network is used to dynamically update the threshold values of sensor node indicators in the specification rule database used by intrusion detection agents. A method for detecting intrusions in wireless sensor networks has been developed and formalized by combining a probabilistic strategy for monitoring the status of a sensor node by intrusion detection agents based on Nash equilibrium, rules for combining sensor node indicators, and detection of anomalous nodes using a multilayer neural network. This made it possible to adaptively track anomalies and detect network-level attacks, preventing the dangerous influence of malicious nodes by blocking them.

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A method for detecting intrusions in wireless sensor networks based on specification rules and an artificial neural network

Abstract

References