Cybersecurity Threat Detection Using AI: A Systematic Review of Approaches

Abstract

The rapid growth of digital technologies and interconnected systems has significantly increased the frequency and complexity of cybersecurity threats. Traditional threat detection methods, which often rely on predefined rules and signature-based techniques, have become insufficient in addressing sophisticated and evolving cyberattacks. As a result, Artificial Intelligence (AI) has emerged as a powerful tool for enhancing cybersecurity threat detection through automated, adaptive, and intelligent analysis of malicious activities. This systematic review explores recent AI-driven approaches used in cybersecurity threat detection, focusing on machine learning, deep learning, and hybrid models applied across diverse threat environments. Following established systematic review protocols, relevant studies were identified, screened, and analyzed to determine prevailing methodologies, application domains, datasets, and evaluation metrics. The findings reveal that supervised and unsupervised machine learning algorithms, such as support vector machines, random forests, and clustering techniques, are widely employed for intrusion detection, malware classification, and anomaly detection. Deep learning architectures, including convolutional and recurrent neural networks, demonstrate improved performance in detecting complex attack patterns in large-scale network traffic. However, challenges such as data imbalance, model interpretability, adversarial attacks, and real-time deployment constraints remain significant barriers to practical implementation. The review highlights emerging trends such as explainable AI, federated learning, and reinforcement learning as promising directions for future research. Overall, this study provides a comprehensive overview of AI-based cybersecurity threat detection strategies and offers insights to guide researchers and practitioners in developing more robust, scalable, and intelligent defense systems.