Cybersecurity Threat Analysis and Behaviour Analysis: A Comprehensive Approach to Proactive Défense

The escalating complexity and frequency of cyber threats have exposed the limitations of traditional reactive cybersecurity measures, necessitating a shift toward proactive and predictive defence strategies. This research paper presents a comprehensive framework that integrates cybersecurity threat analysis and behavior analysis to address the dynamic and evolving nature of modern cyber risks. By combining advanced analytical techniques, such as time-series analysis and machine learning, with insights from behavioral psychology, this study aims to enhance threat detection, prediction, and mitigation while promoting secure user practices. Threat analysis forms the cornerstone of this framework, leveraging time-series analysis (TSA) to identify temporal patterns and anomalies in cybersecurity data, such as network traffic and system logs. Machine learning (ML) techniques, including deep learning and generative adversarial networks (GANs), are employed to detect dynamic malware behaviours and predict emerging threats. These methods enable organizations to move beyond static, rule-based systems and adopt adaptive, data-driven approaches to cybersecurity. Complementing threat analysis, behavior analysis focuses on understanding and monitoring user actions to detect anomalies and mitigate risks. User behaviour analysis (UBA) establishes baselines of normal activity, enabling the identification of deviations that may indicate insider threats or compromised accounts. Additionally, behavioural models, such as the Fogg Behavioural Model (FBM), are applied to design effective cybersecurity awareness programs. By aligning motivation, ability, and prompts, FBM-based interventions have been shown to significantly improve user compliance with security protocols, reducing the human factor in cyber incidents.