Defending Against Evolving Cyber Threats: a Hybrid Framework for Attack Pattern Analysis and Intelligence Integration
Year: 2025
Type: article
Abstract: Advanced Persistent Threats (APTs) represent a sophisticated and evolving class of cyber attacks characterized by stealth, persistence, and targeted objectives. Traditional signature-based security solutions often prove insufficient against these adaptive adversaries, necessitating novel defense mechanisms. This article proposes and reviews a hybrid framework for mitigating APTs, combining behavior profiling and threat intelligence correlation. Behavior profiling establishes a baseline of normal system and user activities, enabling the detection of subtle deviations indicative of malicious intent. Concurrently, threat intelligence correlation enriches these behavioral insights by integrating external, context-rich information about known APT tactics, techniques, and procedures (TTPs). We delve into the methodological foundations of each component and elucidate how their synergistic integration enhances detection accuracy, reduces false positives, and provides actionable insights for proactive threat hunting. By synthesizing current research, this review highlights the empirical advantages of such a combined approach in identifying multi-stage attacks, attributing threat actors, and adapting to the constantly evolving landscape of APTs. Furthermore, we discuss existing limitations and outline crucial future research directions towards building more resilient and intelligent cyber defense systems.
Authors: Prof. Emily Zhang, Luca Romano
Institution: University of California, Berkeley
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4411889050