A Comprehensive Review of Driven Framework for Intelligent Attack Detection and Dynamic Security Monitoring in Modern Digital Infrastructure

Abstract

The rapid growth of cloud computing, Internet of Things (IoT) devices, enterprise networks, and interconnected digital services has increased the complexity of cybersecurity threats. Traditional signature-based security mechanisms often struggle to identify zero-day attacks, evolving malware, and previously unseen malicious activities. This review paper examines the application of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) in intelligent cyberattack detection and dynamic security monitoring. It reviews commonly investigated algorithms, including Random Forest, Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and anomaly detection techniques. Existing research is compared in terms of detection capability, computational requirements, adaptability, false-positive rates, and deployment challenges. The review also examines network traffic analysis, behavioral monitoring, real-time alert generation, and automated incident response. Major challenges include imbalanced datasets, concept drift, adversarial attacks, limited generalization to unseen threats, and the difficulty of deploying computationally intensive models in resource-constrained environments. The review identifies opportunities for hybrid AI architectures, explainable detection, privacy-preserving collaborative learning, and feedback-driven security monitoring. It concludes that combining accurate threat classification with continuous monitoring and carefully controlled response mechanisms represents a promising direction for improving cybersecurity resilience across modern digital infrastructure.