Deep Neural Intelligence for Proactive Cyber Defence: A Comprehensive Review of Emerging Machine Learning Architectures

The growing sophistication of cyber threats has exposed the limitations of traditional rule-based security systems, necessitating the development of intelligent and proactive defence mechanisms. Recent advances in machine learning and deep learning have significantly improved intrusion detection capabilities by enabling automated analysis of large-scale network traffic and early identification of anomalous behavior. This paper presents a comprehensive review of emerging deep neural architectures for proactive cyber defence, focusing on Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, hybrid CNN–LSTM models, and Graph Neural Networks (GNN). The study examines their architectural characteristics, implementation parameters, and comparative performance in detecting advanced cyber threats, including zero-day attacks and persistent intrusions. Furthermore, the paper proposes a hybrid deep neural framework integrated with threat intelligence and automated response mechanisms to enhance detection accuracy and reduce response latency. Key challenges such as computational complexity, false positives, and real-time deployment constraints are critically analyzed. The findings indicate that hybrid and graph-based models demonstrate superior capability in capturing both spatial and relational attack patterns, making them suitable for next-generation Intrusion Detection Systems. This review contributes to the existing body of knowledge by consolidating recent advancements, identifying research gaps, and outlining future directions for scalable, adaptive, and intelligent cyber security solutions capable of addressing the evolving threat landscape.