Forecasting Cyber Threat Intelligence with Memory Augmented Transformer
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Дата
2025
Автори
Науковий керівник
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Видавець
Igor Sikorsky Kyiv Polytechnic Institute
Анотація
Cyber threat intelligence data are volatile, irregular, and shaped by abrupt regime shifts, making accurate forecasting particularly challenging. Motivated by this, we explore the potential of a memory-augmented Transformer forecaster that integrates an evolving memory mechanism andconfidence-regulated attention. Introducing complementary design that enables the model to balance adaptability with stability, remaining robust under noise and structural changes in the threat landscape. Building on and re-architecting the original ACWA-based approach, the resulting ChronoTensor introduced enhanced model achieves parity with state-of-the-art forecasting methods while introducing transparent memory and attention pathways that enhance the interpretability and explainability of its predictions.
Опис
Ключові слова
time series forecasting, adaptive memory mechanisms, cyber threat intelligence, OSINT
Бібліографічний опис
Feher, A. Forecasting Cyber Threat Intelligence with Memory Augmented Transformer / Anatolii Feher // Theoretical and Applied Cybersecurity: scientific journal. – 2025. – Vol. 7, No. 3. – P. 105-113. – Bibliogr.: 17 ref.