Detecting Cyber Attacks through Measurements: Learnings from a Cyber Range

Cybersecurity is an ever-evolving field, with adversaries continuously refining their tactics.Detecting cyber-attacks efficiently and effectively remains a pressing challenge for both governments and enterprises.One promising approach is leveraging cyber ranges-virtual environments that simulate networks, systems, and attacks-for attack detection through precise measurements and monitoring.This paper explores how cyber ranges can be utilized to detect and analyze cyberattacks based on network and system measurements.Through simulated attack scenarios, this study investigates patterns, anomalies, and metrics that can serve as early indicators of malicious behavior.It also assesses the integration of machine learning models to automate detection and reduce false positives.The findings aim to improve real-world threat detection and response systems by informing future detection models with robust, data-driven insights derived from cyber range experiments.