Bridging the Explanation Gap in AI Security: A Task-Driven Approach to XAI Methods Evaluation

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F24%3A00373606" target="_blank" >RIV/68407700:21230/24:00373606 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.5220/0012475200003636" target="_blank" >https://doi.org/10.5220/0012475200003636</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.5220/0012475200003636" target="_blank" >10.5220/0012475200003636</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Bridging the Explanation Gap in AI Security: A Task-Driven Approach to XAI Methods Evaluation

Popis výsledku v původním jazyce

Deciding which XAI technique is best depends not only on the domain, but also on the given task, the dataset used, the model being explained, and the target goal of that model. We argue that the evaluation of XAI methods has not been thoroughly analyzed in the network security domain, which presents a unique type of challenge. While there are XAI methods applied in network security there is still a large gap between the needs of security stakeholders and the selection of the optimal method. We propose to approach the problem by first defining the stack-holders in security and their prototypical tasks. Each task defines inputs and specific needs for explanations. Based on these explanation needs (e.g. understanding the performance, or stealing a model), we created five XAI evaluation techniques that are used to compare and select which XAI method is best for each task (dataset, model, and goal). Our proposed approach was evaluated by running experiments for different security stakehol ders, machine learning models, and XAI methods. Results were compared with the AutoXAI technique and random selection. Results show that our proposal to evaluate and select XAI methods for network security is well-grounded and that it can help AI security practitioners find better explanations for their given tasks.

Název v anglickém jazyce

Bridging the Explanation Gap in AI Security: A Task-Driven Approach to XAI Methods Evaluation

Popis výsledku anglicky

Deciding which XAI technique is best depends not only on the domain, but also on the given task, the dataset used, the model being explained, and the target goal of that model. We argue that the evaluation of XAI methods has not been thoroughly analyzed in the network security domain, which presents a unique type of challenge. While there are XAI methods applied in network security there is still a large gap between the needs of security stakeholders and the selection of the optimal method. We propose to approach the problem by first defining the stack-holders in security and their prototypical tasks. Each task defines inputs and specific needs for explanations. Based on these explanation needs (e.g. understanding the performance, or stealing a model), we created five XAI evaluation techniques that are used to compare and select which XAI method is best for each task (dataset, model, and goal). Our proposed approach was evaluated by running experiments for different security stakehol ders, machine learning models, and XAI methods. Results were compared with the AutoXAI technique and random selection. Results show that our proposal to evaluate and select XAI methods for network security is well-grounded and that it can help AI security practitioners find better explanations for their given tasks.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/VJ02010020" target="_blank" >VJ02010020: AI-Dojo: Multiagentní testbed pro výzkum a testování umělé inteligence v kyberbezpečnosti</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the 16th International Conference on Agents and Artificial Intelligence (Volume 3)

ISBN

978-989-758-680-4

ISSN

2184-3589

e-ISSN

2184-433X

Počet stran výsledku

8

Strana od-do

1370-1377

Název nakladatele

Science and Technology Publications, Lda

Místo vydání

Setúbal

Místo konání akce

Rome

Datum konání akce

24. 2. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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