THE PROBLEM OF INSUFFICIENT ADAPTABILITY OF TRADITIONAL NETWORK SECURITY SYSTEMS TO MODERN CYBER THREATS: ANALYSIS AND SOLUTIONS

Abstract

Traditional network security systems based on static rules and signatures prove ineffective against modern adaptive cyber threats, particularly Advanced Persistent Threats (APTs), zero-day exploits, and AI-generated attacks. Contemporary digital transformation is accompanied by exponential growth of sophisticated cyberattacks characterized by increased complexity, autonomy, and adaptability that fundamentally challenge conventional security paradigms. This study conducts a systematic analysis of open scientific publications from 2020-2025 to investigate the problem of insufficient adaptability in existing security systems and analyse the potential applications of artificial intelligence (AI) and large language models (LLMs) for addressing this challenge.

The research methodology encompasses comprehensive evaluation of various AI-based approaches including ensemble methods, hybrid CNN-GRU architectures, Transformer-based models, and specialized large language models adapted for cybersecurity contexts. Particular attention is given to comparative analysis of effectiveness metrics across standardized datasets including UNSW-NB15, NSL-KDD, CIC-IDS2017, and CSE-CIC-IDS2018.

The research confirms a significant improvement in threat detection speed and accuracy when using AI-based solutions compared to classical approaches, with accuracy rates reaching 87-99.68% while maintaining controlled false positive rates. Ensemble approaches and genetically optimized hybrid CNN-GRU architectures demonstrate exceptional capability for dynamic adaptation to new attack types without manual intervention. Large language models have demonstrated substantial potential in threat intelligence analysis, automated security orchestration, and proactive threat prediction, with specialized models like CyBERT achieving 94.4% accuracy in security claim classification.

However, critical questions arise regarding their resilience to adversarial attacks, interpretability of decision-making processes, and computational overhead in real-world deployment scenarios. The study identifies key challenges including catastrophic forgetting, the need for explainable AI mechanisms, and requirements for continuous learning capabilities to address rapidly evolving threat landscapes. The findings emphasize the transition from reactive to proactive security paradigms through implementation of adaptive, self-learning systems capable of real-time threat prediction.