Adrian Taylor

Li Li, Jean-Pierre S. El Rami, Adrian Taylor et al.

This work aims to enable autonomous agents for network cyber operations (CyOps) by applying reinforcement and deep reinforcement learning (RL/DRL). The required RL training environment is particularly challenging, as it must balance the need for high-fidelity, best achieved through real network emulation, with the need for running large numbers of training episodes, best achieved using simulation. A unified training environment, namely the Cyber Gym for Intelligent Learning (CyGIL) is developed where an emulated CyGIL-E automatically generates a simulated CyGIL-S. From preliminary experimental results, CyGIL-S is capable to train agents in minutes compared with the days required in CyGIL-E. The agents trained in CyGIL-S are transferrable directly to CyGIL-E showing full decision proficiency in the emulated "real" network. Enabling offline RL, the CyGIL solution presents a promising direction towards sim-to-real for leveraging RL agents in real-world cyber networks.

Li Li, Jean-Pierre S. El Rami, Adrian Taylor et al.

Autonomous cyber agents may be developed by applying reinforcement and deep reinforcement learning (RL/DRL), where agents are trained in a representative environment. The training environment must simulate with high-fidelity the network Cyber Operations (CyOp) that the agent aims to explore. Given the complexity of net-work CyOps, a good simulator is difficult to achieve. This work presents a systematic solution to automatically generate a high-fidelity simulator in the Cyber Gym for Intelligent Learning (CyGIL). Through representation learning and continuous learning, CyGIL provides a unified CyOp training environment where an emulated CyGIL-E automatically generates a simulated CyGIL-S. The simulator generation is integrated with the agent training process to further reduce the required agent training time. The agent trained in CyGIL-S is transferrable directly to CyGIL-E showing full transferability to the emulated "real" network. Experimental results are presented to demonstrate the CyGIL training performance. Enabling offline RL, the CyGIL solution presents a promising direction towards sim-to-real for leveraging RL agents in real-world cyber networks.

Sourov Jajodia, Madeena Sultana, Suryadipta Majumdar et al.

Security incident analysis (SIA) poses a major challenge for security operations centers, which must manage overwhelming alert volumes, large and diverse data sources, complex toolchains, and limited analyst expertise. These difficulties intensify because incidents evolve dynamically and require multi-step, multifaceted reasoning. Although organizations are eager to adopt Large Language Models (LLMs) to support SIA, the absence of rigorous benchmarking creates significant risks for assessing their effectiveness and guiding design decisions. Benchmarking is further complicated by: (i) the lack of an LLM-ready dataset covering a wide spectrum of SIA tasks; (ii) the continual emergence of new tasks reflecting the diversity of analyst responsibilities; and (iii) the rapid release of new LLMs that must be incorporated into evaluations. In this paper, we address these challenges by introducing SIABENCH, an agentic evaluation framework for security incident analysis. First, we construct a first-of-its-kind dataset comprising two major SIA task categories: (i) deep analysis workflows for security incidents (25 scenarios) and (ii) alert-triage tasks (135 scenarios). Second, we implement an agent capable of autonomously performing a broad spectrum of SIA tasks (including network and memory forensics, malware analysis across binary/code/PDF formats, phishing email and kit analysis, log analysis, and false-alert detection). Third, we benchmark 11 major LLMs (spanning both open- and closed-weight models) on these tasks, with extensibility to support emerging models and newly added analysis scenarios.

Li Li, Raed Fayad, Adrian Taylor

Given the success of reinforcement learning (RL) in various domains, it is promising to explore the application of its methods to the development of intelligent and autonomous cyber agents. Enabling this development requires a representative RL training environment. To that end, this work presents CyGIL: an experimental testbed of an emulated RL training environment for network cyber operations. CyGIL uses a stateless environment architecture and incorporates the MITRE ATT&CK framework to establish a high fidelity training environment, while presenting a sufficiently abstracted interface to enable RL training. Its comprehensive action space and flexible game design allow the agent training to focus on particular advanced persistent threat (APT) profiles, and to incorporate a broad range of potential threats and vulnerabilities. By striking a balance between fidelity and simplicity, it aims to leverage state of the art RL algorithms for application to real-world cyber defence.