ChatIDS：利用生成式 AI 推进可解释网络安全

Computer Science > Cryptography and Security arXiv:2306.14504 (cs) [Submitted on 26 Jun 2023 (v1), last revised 16 Sep 2026 (this version, v2)] Title:ChatIDS: Advancing Explainable Cybersecurity Using Generative AI Authors:Victor Jüttner, Martin Grimmer, Erik Buchmann View a PDF of the paper titled ChatIDS: Advancing Explainable Cybersecurity Using Generative AI, by Victor J\"uttner and 2 other authors View PDF HTML (experimental) Abstract:Intrusion Detection Systems (IDS) are a proven approach to secure networks. Network-based IDS are typically installed on routers or Internet gateways. This allows them to inspect any incoming or outgoing network traffic, to compare the signatures of network packets with a database of suspicious signatures, or to use artificial intelligence. If the IDS identifies a network connection as suspicious, it sends an alert to the respective user. However, in a privately used network, it is difficult for users without cybersecurity expertise to understand IDS alerts, to distinguish cyberattacks from false alarms, 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. Comments: Subjects: Cryptography and Security (cs.CR) Cite as: arXiv:2306.14504 [cs.CR] (or arXiv:2306.14504v2 [cs.CR] for this version) https://doi.org/10.48550/arXiv.2306.14504 Focus to learn more arXiv-issued DOI via DataCite Journal reference: International Journal On Advances in Security, volume 17, numbers 1 and 2, 2024 Submission history From: Victor Jüttner [view email] [v1] Mon, 26 Jun 2023 08:21:47 UTC (467 KB) [v2] Wed, 16 Sep 2026 15:14:56 UTC (1,916 KB) Full-text links: Access Paper: View a PDF of the paper titled ChatIDS: Advancing Explainable Cybersecurity Using Generative AI, by Victor J\"uttner and 2 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.CR new | recent | 2023-06 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)