SCyTAG Framework Enables Scalable Cyber Twin Generation for Targeted Threat Assessment

Global: SCyTAG Framework Enables Scalable Cyber Twin Generation for Targeted Threat Assessment

Researchers have introduced SCyTAG, a multi-step framework designed to create minimal viable cyber twins that evaluate the impact of specific attack scenarios derived from cyber threat intelligence (CTI) reports. The approach aims to bridge the gap between abstract threat intelligence and practical, scenario‑driven testing within enterprise environments, offering a cost‑effective alternative to full‑scale network emulation.

Framework Overview

SCyTAG begins by ingesting an organization’s network specifications alongside an attack scenario extracted from a CTI report. It then constructs an attack graph (AG) that maps potential pathways an adversary could exploit. Using this graph, the system automatically identifies the subset of network components essential for reproducing the attack, thereby defining the minimal viable cyber twin.

Attack Graph Generation

The attack graph generation module leverages established graph‑theoretic techniques to represent hosts, services, and connectivity. By translating narrative CTI data into structured graph elements, SCyTAG ensures that the resulting model captures the logical flow of the threat while remaining tractable for automated processing.

Cyber Twin Construction

Based on the AG, SCyTAG assembles a cyber twin that includes only the nodes and links required to emulate the specified scenario. This selective inclusion reduces the overall complexity of the emulated environment, allowing organizations to conduct targeted testing without disrupting live production systems.

Evaluation Methodology

The framework was evaluated on both a real organizational network and a synthetic network designed to reflect typical enterprise topologies. In each case, researchers compared the SCyTAG‑generated cyber twin against a full‑topology emulation to assess fidelity, resource consumption, and scalability.

Results and Efficiency Gains

Findings indicate that SCyTAG can trim the number of network components needed for emulation by up to 85% relative to a full topology. Additionally, the resource footprint required for the reduced cyber twin is roughly half of that needed for a complete network replica, while preserving the accuracy of the attack simulation.

Implications for Enterprise Security

By automating the creation of streamlined cyber twins, SCyTAG offers organizations a scalable tool for frequent, scenario‑driven risk assessments. The reduction in required hardware and computational resources makes continuous testing more feasible, potentially enhancing proactive defense postures across diverse enterprise environments.

This report is based on information from arXiv, licensed under Academic Preprint / Open Access. Based on the abstract of the research paper. Full text available via ArXiv.