Explainable Deep Reinforcement Learning for Real-Time Network Intrusion Detection

Authors

• Sebastian Bustamante, University of South AlabamaFollow

Files

Description

This research aims to enhance current Deep Reinforcement Learning (DRL)-based Intrusion Detection System (IDS) models by adding transparency using Explainable Artificial Intelligence (XAI). This study proposes a DRL-based IDS architecture that incorporates explainability to provide interpretable reasons for IDS decisions. A Deep Q-Network (DQN) agent will be trained in a simulated network using well-known datasets to learn traffic behavior. XAI methods will be applied to extract feature importance and allow users to understand why alerts were generated. The outcomes of this research will contribute to improving network security by providing more insight on how XAI could be adopted into modern IDS models, specifically DRL models, to provide a level of transparency and trust for users.

Publication Date

3-2026

Department

Computer Science

Disciplines

Computer Sciences

Recommended Citation

Bustamante, Sebastian, "Explainable Deep Reinforcement Learning for Real-Time Network Intrusion Detection" (2026). Shelby Hall Graduate Research Forum Posters. 51.
https://jagworks.southalabama.edu/southalabama-shgrf-posters/51