A Systematic Review of Behavioral Analysis Techniques for Threat Detection

Abstract

Behavioral analysis has emerged as a critical paradigm in cybersecurity threat detection, addressing the limitations of traditional signature-based and rule-based detection systems. This review paper systematically examines the state-of-the-art approaches in behavioral analysis for threat detection, covering the period from 2020 to 2025. We analyze over 50 recent studies focusing on insider threat detection, advanced persistent threats (APTs), anomaly detection, and real-time threat identification using behavioral patterns. The review encompasses machine learning and deep learning techniques, including Long Short-Term Memory (LSTM) networks, Convolutional Neural Networks (CNNs), Graph Neural Networks (GNNs), and Transformer-based models. We present empirical data from major cybersecurity reports indicating that 60% of organizations experienced insider threats in 2023, with behavioral analytics showing 59% improvement in detecting unknown threats. The paper evaluates commonly used datasets including CMU CERT, UNSW-NB15, and real-world deployment scenarios. Key challenges identified include class imbalance, false positive rates, privacy concerns, and the evolving sophistication of adversarial tactics. We conclude with recommendations for future research directions, emphasizing the integration of federated learning, explainable AI, and hybrid detection architectures to enhance threat detection capabilities while preserving user privacy.