Research and Challenges of Reinforcement Learning in Cyber Defense Decision-Making for Intranet Security

Wang, Wenhao; Sun, Dingyuanhao; Jiang, Feng; Chen, Xingguo; Zhu, Cheng

doi:10.3390/a15040134

Cited by 15 publications

(7 citation statements)

References 100 publications

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“…Namely, the authors focused on penetration-testing, design, response, and recovery as different decision-making tasks for cybersecurity. 9 The response and recovery tasks hold particular interest as they encompass many of the tasks a CSOC may perform. During the response task, defenders must have the capability to detect abnormalities and respond appropriately.…”

Section: Related Workmentioning

confidence: 99%

“…10 Recovery, meanwhile, typically occurs after response and requires defenders to restore functionality or mitigate residual risk from an event. 9 The associated literature with recovery is fairly limited, with some focusing on recovery of critical power infrastructure. 11,12 Separating these tasks into different decision models may have utility in some scenarios, but doing so may not give cybersecurity professionals optimal workflows to enable holistic network health.…”

Section: Related Workmentioning

confidence: 99%

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Autonomous cyber warfare agents: dynamic reinforcement learning for defensive cyber operations

Bierbrauer¹,

Schabinger²,

Carlin³

et al. 2023

Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications V

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In this work, we aim to develop novel cybersecurity playbooks by exploiting dynamic reinforcement learning (RL) methods to close holes in the attack surface left open by the traditional signature-based approach to Defensive Cyber Operations (DCO). A useful first proof-of-concept is provided by the problem of training a scanning defense agent using RL; as a first line of defense, it is important to protect sensitive networks from network mapping tools. To address this challenge, we developed a hierarchical, Monte Carlo-based RL framework for the training of an autonomous agent which detects and reports the presence of Nmap scans in near real-time, efficiently and with near-perfect accuracy. Our algorithm is powered by a reduction of the state space given by a transformer, CLAPBAC, an anomaly detection tool which applies natural language processing to cybersecurity in a manner consistent with state-of-the-art. In a realistic scenario emulated in CyberVAN, our approach generates optimized playbooks for effective defense against malicious insiders inappropriately probing sensitive networks.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Autonomous cyber warfare agents: dynamic reinforcement learning for defensive cyber operations

Bierbrauer¹,

Schabinger²,

Carlin³

et al. 2023

Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications V

View full text Add to dashboard Cite

show abstract

“…The idea of RL was heavily influenced by how most people acquire new skills, namely, by witnessing the results of repeated attempts at the task at hand. [11]. RL excels in real-time and adversarial settings because of its flexibility and utility in modelling an independent agent to conduct consecutive activities, ideally without or with minimal past knowledge of the atmosphere [12].…”

Section: Introductionmentioning

confidence: 99%

Application Study on the Reinforcement Learning Strategies in the Network Awareness Risk Perception and Prevention

Xie

2024

Int J Comput Intell Syst

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The intricacy of wireless network ecosystems and Internet of Things (IoT) connected devices have increased rapidly as technology advances and cyber threats increase. The existing methods cannot make sequential decisions in complex network environments, particularly in scenarios with partial observability and non-stationarity. Network awareness monitors and comprehends the network's assets, vulnerabilities, and ongoing activities in real-time. Advanced analytics, machine learning algorithms, and artificial intelligence are used to improve risk perception by analyzing massive amounts of information, identifying trends, and anticipating future security breaches. Hence, this study suggests the Deep Reinforcement Learning-assisted Network Awareness Risk Perception and Prevention Model (DRL-NARPP) for detecting malicious activity in cybersecurity. The proposed system begins with the concept of network awareness, which uses DRL algorithms to constantly monitor and evaluate the condition of the network in terms of factors like asset configurations, traffic patterns, and vulnerabilities. DRL provides autonomous learning and adaptation to changing network settings, revealing the ever-changing nature of network awareness risks in real time. Incorporating DRL into risk perception increases the system's capacity to recognize advanced attack methods while simultaneously decreasing the number of false positives and enhancing the reliability of risk assessments. DRL algorithms drive dynamic and context-aware response mechanisms, making up the adaptive network prevention component of the development. Predicting new threats and proactively deploying preventive measures, such as changing firewall rules, isolating compromised devices, or dynamically reallocating resources to reduce developing risks, is made possible by the system's ability to learn from historical data and prevailing network activity. The suggested DRL-NARPP model increases the anomaly detection rate by 98.3%, the attack prediction accuracy rate by 97.4%, and the network risk assessment ratio by 96.4%, reducing the false positive ratio by 11.2% compared to other popular methodologies.

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“…This can lead to models that are overfit, meaning they are too narrowly focused on specific attack scenarios and may not generalize well to new or unexpected attack scenarios. In addition to these challenges, there is also a shortage of open-source cybersecurity-based RL experimentation environments that can help researchers address real-world challenges and improve the state of the art in RL cyber applications [ 22 ]. Without access to realistic and scalable experimentation environments, researchers may struggle to develop and test new RL-based approaches to cybersecurity.…”

Section: Introductionmentioning

confidence: 99%

Applying Reinforcement Learning for Enhanced Cybersecurity against Adversarial Simulation

Jeong

Kim

Park

2023

Sensors

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Cybersecurity is a growing concern in today’s interconnected world. Traditional cybersecurity approaches, such as signature-based detection and rule-based firewalls, are often limited in their ability to effectively respond to evolving and sophisticated cyber threats. Reinforcement learning (RL) has shown great potential in solving complex decision-making problems in various domains, including cybersecurity. However, there are significant challenges to overcome, such as the lack of sufficient training data and the difficulty of modeling complex and dynamic attack scenarios hindering researchers’ ability to address real-world challenges and advance the state of the art in RL cyber applications. In this work, we applied a deep RL (DRL) framework in adversarial cyber-attack simulation to enhance cybersecurity. Our framework uses an agent-based model to continuously learn from and adapt to the dynamic and uncertain environment of network security. The agent decides on the optimal attack actions to take based on the state of the network and the rewards it receives for its decisions. Our experiments on synthetic network security show that the DRL approach outperforms existing methods in terms of learning optimal attack actions. Our framework represents a promising step towards the development of more effective and dynamic cybersecurity solutions.

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Research and Challenges of Reinforcement Learning in Cyber Defense Decision-Making for Intranet Security

Cited by 15 publications

References 100 publications

Autonomous cyber warfare agents: dynamic reinforcement learning for defensive cyber operations

Autonomous cyber warfare agents: dynamic reinforcement learning for defensive cyber operations

Application Study on the Reinforcement Learning Strategies in the Network Awareness Risk Perception and Prevention

Applying Reinforcement Learning for Enhanced Cybersecurity against Adversarial Simulation

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