DETECTION OF HYBRID CYBER ATTACKS IN ELECTRONIC COMMUNICATION NETWORKS USING DEEP LEARNING AND INTEGRATED SECURITY SYSTEMS

DOI: 10.31673/2786-8362.2025.024906

Abstract

The article presents a comprehensive approach to modeling electronic communication
networks under hybrid cyber attacks using Zero Trust principles and modern data analysis methods. The
proposed integration of rapid state-change detection and statistical thresholds with multi-level learning
based on convolutional and recurrent neural networks, autoencoders, and visual telemetry fingerprints is
discussed. It has been proven that combining sensor data, network traffic, event logs, and firmware artifacts
into a unified pipeline increases anomaly detection accuracy and reduces response latency in critical
scenarios. The study was conducted considering international standards and framework documents: the
Zero Trust Architecture by the U.S. National Institute of Standards and Technology (NIST SP 800-207),
ISO/IEC 27001 requirements for information security management systems, NIST SP 800-218 (SSDF)
secure software development recommendations, TLS 1.3 and SNMPv3 protocols, as well as the MITRE
ATT&CK methodology for describing and analyzing adversary behavior. The article shows that combining
statistical filtering methods, deep learning, and standardized security policies contributes to the creation of
new tools for security operations and event management centers. From the perspective of the digital
economy, the results support the development of resilient communication infrastructures integrated into
ecosystems of e-services, cloud platforms, and mobile applications. The proposed solutions form a practical
foundation for improving intrusion detection and risk management systems, meet the current requirements
of global markets and cyber resilience strategies, and create conditions for long-term trust in digital
technologies.
Keywords: hybrid cyber attacks, modeling of electronic communication networks, protocol
vulnerabilities, digital economy, international standards, risk management, deep learning

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