In the current landscape where cybersecurity threats are escalating in complexity and frequency, traditional defense mechanisms like rule-based firewalls and signature-based detection are proving inadequate. The dynamism and sophistication of modern cyber-attacks necessitate advanced solutions that can evolve and adapt in real-time. Enter the field of deep reinforcement learning (DRL), a branch of artificial intelligence that has been effectively tackling complex decision-making problems across various domains, including cybersecurity. In this study, we advance the field by implementing a DRL framework to simulate cyber-attacks, drawing on authentic scenarios to enhance the realism and applicability of the simulations. By meticulously adapting DRL algorithms to the nuanced requirements of cybersecurity contexts—such as custom reward structures and actions, adversarial training, and dynamic environments—we provide a tailored approach that significantly improves upon traditional methods. Our research undertakes a thorough comparative analysis of three sophisticated DRL algorithms—deep Q-network (DQN), actor–critic, and proximal policy optimization (PPO)—against the traditional RL algorithm Q-learning, within a controlled simulation environment reflective of real-world cyber threats. The findings are striking: the actor–critic algorithm not only outperformed its counterparts with a success rate of 0.78 but also demonstrated superior efficiency, requiring the fewest iterations (171) to complete an episode and achieving the highest average reward of 4.8. In comparison, DQN, PPO, and Q-learning lagged slightly behind. These results underscore the critical impact of selecting the most fitting algorithm for cybersecurity simulations, as the right choice leads to more effective learning and defense strategies. The impressive performance of the actor–critic algorithm in this study marks a significant stride towards the development of adaptive, intelligent cybersecurity systems capable of countering the increasingly sophisticated landscape of cyber threats. Our study not only contributes a robust model for simulating cyber threats but also provides a scalable framework that can be adapted to various cybersecurity challenges.

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.

Precision medicine is a new approach to understanding health and disease based on patient-specific data such as medical diagnoses; clinical phenotype; biologic investigations such as laboratory studies and imaging; and environmental, demographic, and lifestyle factors. The importance of machine learning techniques in healthcare has expanded quickly in the last decade owing to the rising availability of vast multi-modality data and developed computational models and algorithms. Reinforcement learning is an appealing method for developing efficient policies in various healthcare areas where the decision-making process is typically defined by a long period or a sequential process. In our research, we leverage the power of reinforcement learning and electronic health records of South Koreans to dynamically recommend treatment prescriptions, which are personalized based on patient information of hypertension. Our proposed reinforcement learning-based treatment recommendation system decides whether to use mono, dual, or triple therapy according to the state of the hypertension patients. We evaluated the performance of our personalized treatment recommendation model by lowering the occurrence of hypertension-related complications and blood pressure levels of patients who followed our model’s recommendation. With our findings, we believe that our proposed hypertension treatment recommendation model could assist doctors in prescribing appropriate antihypertensive medications.