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Contribution Details
| Type | Bachelor's Thesis |
| Scope | Discipline-based scholarship |
| Title | Computing the Trustworthiness Level of Unsupervised AI-based Intrusion Detection Systems |
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| Institution | University of Zurich |
| Faculty | Faculty of Business, Economics and Informatics |
| Date | 2022 |
| Abstract Text | Systems utilizing artificial intelligence (AI) are becoming more and more useful in supporting human decision-making tasks. Unsupervised AI algorithms play a significant role in the detection of intrusions and cyberattacks on devices with limited resources. However, current solutions focus on achieving the best detection performance, missing the importance of quantifying the trustworthiness level of the trained models and their predictions. This work focuses on computing the level of trustworthiness for unsupervised anomaly detection models. In this work a taxonomy with four different pillars of trust and associated metrics is proposed. Further, an algorithm has been developed which takes unsupervised anomaly detection models together with the underlying training, test and outlier data sets as inputs to compute an overall trust score. This algorithm has further on been embedded in a web application which is available to model developers and serves as a global solution to evaluate unsupervised anomaly detection models towards trustworthiness. Lastly, an in-depth analysis on different models has been conducted in order to evaluate the proposed algorithm and to point out strengths and limitations. |
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