Interpretable Cyber Threat Detection for Enterprise Industrial Networks: A Computational Design Science Approach

• URL: http://arxiv.org/abs/2409.03798v1
• Date: Wed, 4 Sep 2024 19:54:28 GMT
• Title: Interpretable Cyber Threat Detection for Enterprise Industrial Networks: A Computational Design Science Approach
• Authors: Prabhat Kumar, A. K. M. Najmul Islam,
• Abstract summary: We use IS computational design science paradigm to develop a two-stage cyber threat detection system for enterprise-level IS. 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.
• Score: 1.935143126104097
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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