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Increasing the Trustworthiness ofAI-based In-Vehicle IDS usingeXplainable AI

An in-vehicle intrusion detection system (IV-IDS) is one of the protection mechanisms used to detect cyber attacks on electric or autonomous vehicles where anomaly-based IDS solution have better potential at detecting the attacks especially zero-day attacks. Generally, the IV-IDS generate false alarms (falsely detecting normal data as attacks) because of the difficulty to differentiate between normal and attack data. It can lead to undesirable situations, such as increased laxness towards the system, or uncertainties in the event-handling following a generated alarm. With the help of sophisticated Artificial Intelligence (AI) models, the IDS improves the chances of detecting attacks. However, the use of such a model comes at the cost of decreased interpretability, a trait that is argued to be of importance when ascertaining various other valuable desiderata, such as a model’s trust, causality, and robustness. Because of the lack of interpretability in sophisticated AI-based IV-IDSs, it is difficult for humans to trust such systems, let alone know what actions to take when an IDS flags an attack. By using tools found in the area of eXplainable AI (XAI), this thesis aims to explore what kind of explanations could be produced in accord with model predictions, to further increase the trustworthiness of AI-based IV-IDSs. Through a comparative survey, aspects related to trustworthiness and explainability are evaluated on a custom, pseudo-global, visualization-based explanation (”VisExp”), and a rule based explanation. The results show that VisExp increase the trustworthiness,and enhanced the  explainability of the AI-based IV-IDS.

Identiferoai:union.ndltd.org:UPSALLA1/oai:DiVA.org:miun-45223
Date January 2022
CreatorsLundberg, Hampus
PublisherMittuniversitetet, Institutionen för informationssystem och –teknologi
Source SetsDiVA Archive at Upsalla University
LanguageEnglish
Detected LanguageEnglish
TypeStudent thesis, info:eu-repo/semantics/bachelorThesis, text
Formatapplication/pdf
Rightsinfo:eu-repo/semantics/openAccess

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