Temporal Eclectic Rule Extraction: Exploring Trustworthy Explainable Artificial Intelligence for Recurrent Neural Networks

Authors

• Micah Israel, University of South AlabamaFollow

Files

Description

Enhancing temporal neural network interpretability can greatly increase the effectiveness of Intrusion Detection Systems (IDS). While explainable Deep Neural Networks (DNN) have been researched heavily in the literature for intrusion detection, explainable temporal neural networks lack the same attention. Current state-of-the-art XAI techniques rely on black-box surrogate explainers, which attempt to generate post-hoc explanations without valuable information inside the model's hidden neurons. To address this, this proposal introduces a novel white-box XAI method, Temporal Eclectic Rule Extraction (TERE), which is designed to provide explainable rules directly from temporal models. TERE aims to enhance decision transparency in IDS by offering interpretable explanations for time-series data generated by a white-box algorithm.

Publication Date

3-2026

Department

Computer Science

Disciplines

Computer Sciences

Recommended Citation

Israel, Micah, "Temporal Eclectic Rule Extraction: Exploring Trustworthy Explainable Artificial Intelligence for Recurrent Neural Networks" (2026). Shelby Hall Graduate Research Forum Posters. 62.
https://jagworks.southalabama.edu/southalabama-shgrf-posters/62