Interpretable Cyber Threat Detection for Enterprise Industrial Networks: A Computational Design Science Approach
<|reference_start|>Interpretable Cyber Threat Detection for Enterprise Industrial Networks: A Computational Design Science Approach: Enterprise industrial networks face threats that risk data and operations. However, designing efficient threat detection system is challenging due to data scarcity, especially where privacy is a concern. The complexity of enterprise industrial network data adds to this challenge, causing high false positives and interpretation issues. Towards this, we use IS computational design science paradigm to develop a two-stage cyber threat detection system for enterprise-level IS that are both secure and capable of adapting to evolving technological and business environments. The first stage generates synthetic industrial network data using a modified generative adversarial network. The second stage develops a novel bidirectional gated recurrent unit and a modified attention mechanism for effective threat detection. We also use shapley additive explanations and a decision tree technique for enhancing interpretability. Our analysis on two public datasets shows the frameworks high precision in threat detection and offers practical cybersecurity solutions and methodological advancements.<|reference_end|>
@article{kumar2024interpretable,
    title={Interpretable Cyber Threat Detection for Enterprise Industrial Networks:
  A Computational Design Science Approach},
    author={Prabhat Kumar and A.K.M. Najmul Islam},
    journal={arXiv preprint arXiv:2409.03798},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.03798},
    primaryClass={cs.CR}
}