Home // International Journal On Advances in Security, volume 17, numbers 1 and 2, 2024 // View article

ChatIDS: Advancing Explainable Cybersecurity Using Generative AI

Authors:
Victor Jüttner
Martin Grimmer
Erik Buchmann

Keywords: Intrusion Detection, ChatGPT, Smart Home

Abstract:
An intrusion detection system (IDS) is a proven approach to securing networks. Network-based IDS solutions are typically installed on routers or Internet gateways. They can inspect all incoming and outgoing network traffic, compare network packet signatures against a database of suspicious signatures, or use artificial intelligence. If the IDS identifies a network connection as suspicious, it sends an alert to the user. However, on a home network, it is difficult for users without cybersecurity expertise to understand IDS alerts, distinguish cyberattacks from false alarms, and take appropriate action in a timely manner. This puts the security of home networks, smart home installations, home office workers, etc. at risk, even if an IDS is properly installed and configured. In this work, we propose ChatIDS, our approach to explain IDS alerts to non-experts using large language models. We evaluate the feasibility of ChatIDS using ChatGPT and identify open research questions with the help of interdisciplinary experts in artificial intelligence. Potential issues in areas such as trust, privacy, ethics, etc. need to be addressed before ChatIDS can be put into practice. Our results show that ChatIDS has the potential to improve network security by suggesting meaningful security measures from IDS alerts in an intuitive language.

Pages: 72 to 81

Copyright: Copyright (c) to authors, 2024. Used with permission.

Publication date: June 30, 2024

Published in: journal

ISSN: 1942-2636