Al-Based Network and Communication Security: Challenges, Solutions, and Future Prospects

This systematic review explores the critical role of artificial intelligence (AI) in bolstering network and communication security against the backdrop of rapidly evolving cyber threats. Drawing from a comprehensive analysis of recent literature, it identifies a significant gap between the pace of threat advancement and organizational security preparedness, leading to data breach costs averaging $۴.۴۴ million globally, as reported in ۲۰۲۵, with a ۹% decrease from the previous year amid rising AI-related incidents, including ۱۳% of breaches involving AI without proper oversight. Key challenges encompass technical issues like scalability in large-scale systems, data quality deficiencies, and adversarial attacks, as well as ethical and legal concerns including AI decision transparency and regulatory frameworks. Solutions proposed include the integration of lightweight blockchain for secure metadata sharing to enhance distributed analysis and integrity, leveraging platforms such as TensorFlow or PyTorch with hardware accelerators for efficient model training, and adopting continuous learning mechanisms to adapt to novel threats. Empirical evaluations demonstrate that AI-driven approaches, particularly the proposed hybrid model, achieve detection accuracies up to ۹۵%, significantly outperforming traditional rule-based methods. The review advocates for hybrid architectures combining deep learning and reinforcement learning to improve resilience, particularly in emerging environments like quantum and ۶G networks. Future directions emphasize quantum-resistant cryptography, ethical AI governance including human-in-the-loop intelligence, and interdisciplinary collaborations to foster trustworthy, adaptive security systems. This work synthesizes insights from over ۷۰ primary studies, offering a roadmap for researchers and practitioners to advance AI-centric cybersecurity.

کلیدواژه ها:

Network Security ، Artificial Intelligence ، Anomaly Detection ، Communication Security ، Machine Learning for Intrusion Prevention ، Adversarial Attacks ، Blockchain Integration