arXiv subjects

Andres Murillo

Publications and source records attributed to Andres Murillo.

4 recordsLinked to original sources

arXiv2026

COHORT: Collaborative Orchestration for Hardening via Offensive Replay on Emulated Topologies

Mitigating an observed adversary in an enterprise network typically takes weeks of expert work: an analyst derives a mitigation tailored to that adversary, validates it without breaking production, and verifies it disrupts the specific attack. The procedure relies on expert judgment and cannot safely be exercised against the production network. COHORT is the first end-to-end framework to automate this procedure for deployable mitigations. A role-decomposed multi-agent LLM workflow proposes candidates, implements them as real device commands, and refines them through a critique loop, all on a high-fidelity GNS3 emulator running real vendor firmware (firewall, switch, router). Each candidate is evaluated by offensive replay: re-executing the original adversary on the mitigated network for a paired comparison against the unmitigated baseline, rather than the reward-signal or expert-judgment proxies used in prior simulation, hybrid, and configuration-generation work. Two further checks complement replay: a connectivity-regression check (LAN ping and internet HTTP probe) rejects mitigations that disrupt legitimate LAN or internet connectivity, and a cumulative evaluation stacks approved mitigations onto a persistent state to surface compound effects. Across three topologies and four attack scenarios (ransomware, lateral movement, DNS exfiltration, data theft), 46.7% of generated mitigations both disrupt the attack and preserve connectivity under replay, 4.4 times the rate of a single-agent baseline using the same model and tool access. A demo video walking through the framework is available with our released artifacts.

cs.NI↗

arXiv2025

SCyTAG: Scalable Cyber-Twin for Threat-Assessment Based on Attack Graphs

Understanding the risks associated with an enterprise environment is the first step toward improving its security. Organizations employ various methods to assess and prioritize the risks identified in cyber threat intelligence (CTI) reports that may be relevant to their operations. Some methodologies rely heavily on manual analysis (which requires expertise and cannot be applied frequently), while others automate the assessment, using attack graphs (AGs) or threat emulators. Such emulators can be employed in conjunction with cyber twins to avoid disruptions in live production environments when evaluating the highlighted threats. Unfortunately, the use of cyber twins in organizational networks is limited due to their inability to scale. In this paper, we propose SCyTAG, a multi-step framework that generates the minimal viable cyber twin required to assess the impact of a given attack scenario. Given the organizational computer network specifications and an attack scenario extracted from a CTI report, SCyTAG generates an AG. Then, based on the AG, it automatically constructs a cyber twin comprising the network components necessary to emulate the attack scenario and assess the relevance and risks of the attack to the organization. We evaluate SCyTAG on both a real and fictitious organizational network. The results show that compared to the full topology, SCyTAG reduces the number of network components needed for emulation by up to 85% and halves the amount of required resources while preserving the fidelity of the emulated attack. SCyTAG serves as a cost-effective, scalable, and highly adaptable threat assessment solution, improving organizational cyber defense by bridging the gap between abstract CTI and practical scenario-driven testing.

cs.CR↗

arXiv2024

Labeling NIDS Rules with MITRE ATT&CK Techniques: Machine Learning vs. Large Language Models

Analysts in Security Operations Centers (SOCs) are often occupied with time-consuming investigations of alerts from Network Intrusion Detection Systems (NIDS). Many NIDS rules lack clear explanations and associations with attack techniques, complicating the alert triage and the generation of attack hypotheses. Large Language Models (LLMs) may be a promising technology to reduce the alert explainability gap by associating rules with attack techniques. In this paper, we investigate the ability of three prominent LLMs (ChatGPT, Claude, and Gemini) to reason about NIDS rules while labeling them with MITRE ATT&CK tactics and techniques. We discuss prompt design and present experiments performed with 973 Snort rules. Our results indicate that while LLMs provide explainable, scalable, and efficient initial mappings, traditional Machine Learning (ML) models consistently outperform them in accuracy, achieving higher precision, recall, and F1-scores. These results highlight the potential for hybrid LLM-ML approaches to enhance SOC operations and better address the evolving threat landscape.

cs.CR↗

arXiv2024

GeNet: A Multimodal LLM-Based Co-Pilot for Network Topology and Configuration

Communication network engineering in enterprise environments is traditionally a complex, time-consuming, and error-prone manual process. Most research on network engineering automation has concentrated on configuration synthesis, often overlooking changes in the physical network topology. This paper introduces GeNet, a multimodal co-pilot for enterprise network engineers. GeNet is a novel framework that leverages a large language model (LLM) to streamline network design workflows. It uses visual and textual modalities to interpret and update network topologies and device configurations based on user intents. GeNet was evaluated on enterprise network scenarios adapted from Cisco certification exercises. Our results demonstrate GeNet's ability to interpret network topology images accurately, potentially reducing network engineers' efforts and accelerating network design processes in enterprise environments. Furthermore, we show the importance of precise topology understanding when handling intents that require modifications to the network's topology.

cs.NI↗