ChatIDS: Explainable Cybersecurity Using Generative AI

TL;DR

ChatIDS leverages large language models to explain IDS alerts in plain language, aiming to improve cybersecurity awareness and response for non-expert users in private networks.

Contribution

The paper introduces ChatIDS, a novel approach that uses generative AI to make IDS alerts understandable for non-experts, addressing a key usability gap.

Findings

ChatIDS can generate meaningful security advice from IDS alerts.

Feasibility demonstrated using ChatGPT with positive expert feedback.

Open issues in trust, privacy, and ethics identified for future work.

Abstract

Intrusion Detection Systems (IDS) are a proven approach to secure networks. However, in a privately used network, it is difficult for users without cybersecurity expertise to understand IDS alerts, and to respond in time with adequate measures. This puts the security of home networks, smart home installations, home-office workers, etc. at risk, even if an IDS is correctly installed and configured. In this work, we propose ChatIDS, our approach to explain IDS alerts to non-experts by using large language models. We evaluate the feasibility of ChatIDS by using ChatGPT, and we identify open research issues with the help of interdisciplinary experts in artificial intelligence. Our results show that ChatIDS has the potential to increase network security by proposing meaningful security measures in an intuitive language from IDS alerts. Nevertheless, some potential issues in areas such as trust, privacy, ethics, etc. need to be resolved, before ChatIDS might be put into practice.