Using Generative AI Models to Support Cybersecurity Analysts

One of the tasks of security analysts is to detect security vulnerabilities and ongoing attacks. There is already a large number of software tools that can help to collect security-relevant data, such as event logs, security settings, application manifests, and even the (decompiled) source code of potentially malicious applications. The analyst must study these data, evaluate them, and properly identify and classify suspicious activities and applications. Fast advances in the area of Artificial Intelligence have produced large language models that can perform a variety of tasks, including generating text summaries and reports. In this article, we study the potential black-box use of LLM chatbots as a support tool for security analysts. We provide two case studies: the first is concerned with the identification of vulnerabilities in Android applications, and the second one is concerned with the analysis of security logs. We show how LLM chatbots can help security analysts in their work, but point out specific limitations and security concerns related to this approach.

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Publication Details

Journal
Electronics
Published
2024-11-28
DOI
https://doi.org/10.3390/electronics13234718
Citations
6
Primary Topic
Network Security and Intrusion Detection
Type
article
Field-Weighted Citation Impact
2.09

Funders

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article

Using Generative AI Models to Support Cybersecurity Analysts

Pavol Zajac, Štefan Balogh, Marek Mlynček, Oliver Vraňák
6 citations
Electronics
Network Security and Intrusion Detection
2.09
article

Using Generative AI Models to Support Cybersecurity Analysts

Pavol Zajac, Štefan Balogh, Marek Mlynček, Oliver Vraňák
article en
6 citations

Abstract

One of the tasks of security analysts is to detect security vulnerabilities and ongoing attacks. There is already a large number of software tools that can help to collect security-relevant data, such as event logs, security settings, application manifests, and even the (decompiled) source code of potentially malicious applications. The analyst must study these data, evaluate them, and properly identify and classify suspicious activities and applications. Fast advances in the area of Artificial Intelligence have produced large language models that can perform a variety of tasks, including generating text summaries and reports. In this article, we study the potential black-box use of LLM chatbots as a support tool for security analysts. We provide two case studies: the first is concerned with the identification of vulnerabilities in Android applications, and the second one is concerned with the analysis of security logs. We show how LLM chatbots can help security analysts in their work, but point out specific limitations and security concerns related to this approach.

ElectronicsVol. 13(23)
Slovak University of Technology in Bratislava (SK)
Agentúra na Podporu Výskumu a Vývoja
Peace, Justice and strong institutions
Openalex Percentile: Top 12%
Network Security and Intrusion Detection
2.09
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Using Generative AI Models to Support Cybersecurity Analysts — Pavol Zajac, Štefan Balogh, et al. · Electronics (2024) | TGRS Research Map | TGRS