CybORG: An Autonomous Cyber Operations Research Gym

Baillie, Callum; Standen, Maxwell; Schwartz, Jonathon; Docking, Michael; Bowman, David; Kim, Junae

doi:10.48550/arxiv.2002.10667

Cited by 10 publications

(19 citation statements)

References 16 publications

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Section: Related Workmentioning

confidence: 76%

“…A similar experience to [12] was described more recently by [18] which targets research in Autonomous Cyber Operations (ACO). The authors of [18] proposed a high-fidelity emulation using VMs, and a low-fidelity simulation using a finite state machine design that could model networks. Virtualization of the different scenarios was realized by using a description file (YAML) and a flag determined the use of either simulation or emulation.…”

Section: Related Workmentioning

confidence: 76%

“…OS-virtualization is typically referred to as "containers" in Linux [31], and is widely considered the most efficient virtualization method for its highest performance and fastest "start-up" time. Different approaches to simulation are presented at [13], [14], [15], [16], [17], [18].…”

Section: Related Workmentioning

confidence: 99%

“…This was raised by [12] however Handigol et al [32] and Heller [33] demonstrated how containers, could be used without a relevant loss of fidelity, which further encourages the use of containerization. Other approaches to emulation include [8], [9], [10], [11], [17] or [18].…”

Section: Related Workmentioning

confidence: 99%

“…Similar to [18], alurity toolbox allows to build scenarios using a simple description file in YAML format. Listing 1 provides an example of such format while modeling the scenario of Figure 2a.…”

Section: Aluritymentioning

confidence: 99%

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alurity, a toolbox for robot cybersecurity

Mayoral-Vilches,

Abad-Fernández,

Pinzger

et al. 2020

Preprint

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The reuse of technologies and inherent complexity of most robotic systems is increasingly leading to robots with wide attack surfaces and a variety of potential vulnerabilities. Given their growing presence in public environments, security research is increasingly becoming more important than in any other area, specially due to the safety implications that robot vulnerabilities could cause on humans. We argue that security triage in robotics is still immature and that new tools must be developed to accelerate the testing-triage-exploitation cycle, necessary for prioritizing and accelerating the mitigation of flaws.The present work tackles the current lack of offensive cybersecurity research in robotics by presenting a toolbox and the results obtained with it through several use cases conducted over a year period. We propose a modular and composable toolbox for robot cybersecurity: alurity. By ensuring that both roboticists and security researchers working on a project have a common, consistent and easily reproducible development environment, alurity aims to facilitate the cybersecurity research and the collaboration across teams.

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Section: Related Workmentioning

confidence: 76%

Section: Related Workmentioning

confidence: 76%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Aluritymentioning

confidence: 99%

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alurity, a toolbox for robot cybersecurity

Mayoral-Vilches,

Abad-Fernández,

Pinzger

et al. 2020

Preprint

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Modelling penetration testing with reinforcement learning using capture‐the‐flag challenges: Trade‐offs between model‐free learning and a priori knowledge

Zennaro

Erdődi

2023

IET Information Security

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Penetration testing is a security exercise aimed at assessing the security of a system by simulating attacks against it. So far, penetration testing has been carried out mainly by trained human attackers and its success critically depended on the available expertise. Automating this practice constitutes a non-trivial problem because of the range and complexity of actions that a human expert may attempt. The authors focus their attention on simplified penetration testing problems expressed in the form of capture the flag hacking challenges, and analyse how model-free reinforcement learning algorithms may help solving them. In modelling these capture the flag competitions as reinforcement learning problems the authors highlight the specific challenges that characterize penetration testing. The authors show how this challenge may be eased by relying on different forms of prior knowledge that may be provided to the agent. Since complexity scales exponentially as soon as the set of states and actions for the reinforcement learning agent is extended, the need to restrict the exploration space by using techniques to inject a priori knowledge is highlighted, thus making it possible to achieve solutions more efficiently. K E Y W O R D Scapture the flag, imitation learning, penetration testing, Q-learning, reinforcement learningThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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A Comprehensive Survey: Evaluating the Efficiency of Artificial Intelligence and Machine Learning Techniques on Cyber Security Solutions

Ozkan-Okay,

Akin,

Aslan

et al. 2024

IEEE Access

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Given the continually rising frequency of cyberattacks, the adoption of artificial intelligence methods, particularly Machine Learning (ML), Deep Learning (DL), and Reinforcement Learning (RL), has become essential in the realm of cybersecurity. These techniques have proven to be effective in detecting and mitigating cyberattacks, which can cause significant harm to individuals, organizations, and even countries. Machine learning algorithms use statistical methods to identify patterns and anomalies in large datasets, enabling security analysts to detect previously unknown threats. Deep learning, a subfield of ML, has shown great potential in improving the accuracy and efficiency of cybersecurity systems, particularly in image and speech recognition. On the other hand, RL is again a subfield of machine learning that trains algorithms to learn through trial and error, making it particularly effective in dynamic environments. We also evaluated the usage of ChatGPT-like AI tools in cyber-related problem domains on both sides, positive and negative. This article provides an overview of how ML, DL, and RL are applied in cybersecurity, including their usage in malware detection, intrusion detection, vulnerability assessment, and other areas. The state-of-theart studies using ML, DL, and RL models are evaluated in each section based on the main idea, techniques, and important findings. It also discusses these techniques' challenges and limitations, including data quality, interpretability, and adversarial attacks. Overall, the use of ML, DL, and RL in cybersecurity holds great promise for improving the effectiveness of security systems and enhancing our ability to protect against cyberattacks. However, it is essential to continue developing and refining these techniques to address the ever-evolving nature of cyber threats. Besides, some promising solutions that rely on machine learning, deep learning, and reinforcement learning are susceptible to adversarial attacks, underscoring the importance of factoring in this vulnerability when devising countermeasures against sophisticated cyber threats. We also concluded that ChatGPT can be a valuable tool for cybersecurity, but it should be noted that ChatGPT-like tools can also be manipulated to threaten the integrity, confidentiality, and availability of data.INDEX TERMS cyberattacks and solutions; deep learning; machine learning; reinforcement learning, AI tools I. INTRODUCTIONTechnology in every aspect of our lives provides us with many conveniences but also causes several problems. One of VOLUME 4, 2016

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CybORG: An Autonomous Cyber Operations Research Gym

Cited by 10 publications

References 16 publications

alurity, a toolbox for robot cybersecurity

alurity, a toolbox for robot cybersecurity

Modelling penetration testing with reinforcement learning using capture‐the‐flag challenges: Trade‐offs between model‐free learning and a priori knowledge

A Comprehensive Survey: Evaluating the Efficiency of Artificial Intelligence and Machine Learning Techniques on Cyber Security Solutions

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