# Q-Learning Approach Applied to Network Security
## 1 Introduction  
With the evolution of Artificial Intelligence (AI), our society faces unique, unknown cyber-attacks and challenges. Therefore, we must seek contemporary methods and security mechanisms to protect our systems. Reinforcement learning has been successfully applied in robotics; numerous researchers are interested in its application in network security and, more precisely, in intrusion detection (ID) and intrusion response (IR) systems. The primary benefit of reinforcement learning is the opportunity for a decision agent to interact with the environment and acquire a reward or a penalty from it. After receiving this feedback, the agent can learn from his mistakes, adjust his behavior, and make an optimal decision based on some expected rewards. The environment is represented as a Markov Decision Process (MDP), where the agent finds himself in a particular state, takes action, receives feedback, and transitions to another state. This study aims to provide an automated intrusion response mechanism that could complement the currently developed machine learning methods for intrusion detection systems and enhance the system's security. The study focuses on creating a conceptual module with a practical simulation and implication that will advance the existing IDRS. The objective of the ID is to locate unusual or suspicious network property behavior in a particular system. Conventional approaches focus on signatures of known attacks and compare traffic against these signatures, but cannot identify new attack types using other signatures. Anomaly detection methods can resolve this problem and do not require preliminary signature knowledge. Those approaches begin by analyzing a time-series sequence of features categorized as \"normal\" or \"attack\". The prevalence of studies is consolidated around network-based IDS, and minimal research has been conducted on what to do next once an anomaly has been detected. In this work, we aim to administer these existing methods in conjunction with providing an automated machine learning mechanism for the subsequent steps that could be undertaken onceanomaly detection has occurred. This actual reinforcement learning mechanism works in the environment of the intrusion response mechanism. It observes network control protocols and the overall health of the network and, based on IDS signals, automatically takes proper actions to handle malicious or unauthorized activities by stopping them from deteriorating and returning the system. It takes action against malicious traffic in an automated manner without requiring administrative or human intervention. Our contribution is the representation of the probabilistic structure of the intrusion response model as a Markov Decision Process, and provides a realistic implementation of Q-learning in the IR system. The analytical part of the paper provides evidence that this probabilistic structure is plausible and efficient, and its implementation could enhance security and offer a comprehensive, adaptable, automated machine learning solution to cybersecurity's rapidly evolving environment. First, we provide relevant literature on the RL applied to IDRS in network security. We then describe conceptually the main algorithmic structure of the Q-learning model selected for our IR mechanism and outline the proposed algorithm's application to network security. Results are reported and compared to value iteration, policy iteration, and linear programming RL methods. Ultimately, we will discuss some limitations and potential future improvements that could provide a more realistic and profound understanding of using the Q-learning approach in network security.