Neurosymbolic Learning for Advanced Persistent Threat Detection under Extreme Class Imbalance
The growing deployment of Internet of Things (IoT) devices in smart cities and industrial environments increases vulnerability to stealthy, multi-stage advanced persistent threats (APTs) that exploit wireless communication. Detection is challenging due to severe class imbalance in network traffic, which limits the effectiveness of traditional deep learning approaches and their lack of explainability in classification decisions. To address these challenges, this paper proposes a neurosymbolic architecture that integrates an optimized BERT model with logic tensor networks (LTN) for explainable APT detection in wireless IoT networks. The proposed method addresses the challenges of mobile IoT...
| Authors | Quhura Fathima, Neda Moghim, Mostafa Taghizade Firouzjaee et al. |
|---|---|
| Primary category | cs.CR |
| Published | 28 February 2026 |
| Last updated at source | 28 February 2026 |
Silytics shows public metadata from arXiv and links to the original. Information may be incomplete or out of date. Last refreshed 17 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.