Intrusion Detection System Using Reinforcement Learning Block Diagram

This paper proposes ID-RDRL, an intrusion detection method with feature selection based on deep reinforcement learning, as a solution to the current issues faced by intrusion detection systems

Intrusion Detection Systems IDS are crucial for securing IoT however, traditional IDS often struggle to adapt to IoT networks' dynamic and evolving nature and threat patterns. A potential solution is using Deep Reinforcement Learning DRL to enhance IDS adaptability, enabling them to learn from and react to their operational

The Reinforcement Learning RL agent is trained using the Deep Deterministic Policy Gradient DDPG to provide corrective actions for the IoT devices under attack. The RL agent ensures proactive defense by adjusting mitigation strategies based on the type of attack. The RL agent's key functions State Represents the IoT device's features and the environment state.

A reinforcement learning approach for host-based intrusion detection using sequences of system calls Proceedings of international conference on intelligent computing , Lecture notes in computer science, LNCS , Vol. 3644 2005 , pp. 995 - 1003

Intrusion Detection Systems IDS play a critical role in ensuring the security and integrity of computer networks by identifying and mitigating potential threats. Feature selection is a fundamental task in IDS, aiming to select a subset of relevant features from the vast amount of available data to improve detection accuracy and reduce computational overhead. Traditional feature selection

The research aims to develop a novel Intrusion Detection System IDS using computational intelligence, specifically focusing on a hybrid reinforcement learning approach. Unlike traditional IDS that rely on static rule-based approaches, this IDS will dynamically adapt and learn from network traffic patterns, allowing it to detect and respond to

Our research on the efficacy of deep reinforcement learning helps us comprehend the challenges encountered by NIDS DRL. To find network anomalies, we suggest integrating AdversarialMulti Agent Reinforcement Learning with Deep QLearning AE-DQN. We compare our suggestions on the NSL-KDD dataset with the KDDTest dataset. In this article, we take a look at the difficulty of reducing an

As cyber threats grow in complexity, the demand for intelligent and adaptive intrusion detection systems IDS is more critical than ever. Traditional machine learning models, while effective, often struggle to keep up with the dynamic and evolving nature of cyberattacks. This chapter presents an advanced approach to network intrusion detection using reinforcement learning RL, a machine

A context-aware robust intrusion detection system a reinforcement learning-based approach. Int. J. Inf. Secur., 19 6 2020, pp. 657-678, 10.1007s10207-019-00482-7. Network intrusion detection systems using adversarial reinforcement learning with deep Q-network. 2020 18th International Conference on ICT and Knowledge Engineering

This repository contains the code for the project quotIDS-ML Intrusion Detection System Development Using Machine Learningquot. The code and proposed Intrusion Detection System IDSs are general models that can be used in any IDS and anomaly detection applications. In this project, three papers have been published