Submitted:
14 January 2026
Posted:
15 January 2026
You are already at the latest version
Abstract
This paper presents a foundational model engineered to bolster cybersecurity posture through advanced network traffic intelligence. Recognizing the escalating complexity of cyber threats and the limitations of traditional anomaly detection, we introduce a novel deep learning architecture that extracts profound, actionable insights from raw network flow data. This model is specifically designed to overcome challenges posed by encrypted traffic and diverse attack vectors, enabling the identification of subtle indicators of compromise. By providing a comprehensive understanding of network behavior, our solution empowers real-time threat analysis, accelerates intrusion detection, and strengthens overall cyber resilience against sophisticated and evolving digital adversaries.
Keywords:
cybersecurity intelligence
; network traffic analysis
; foundation models
; self-supervised learning
; deep learning
; T5 architecture
; traffic tokenization
; masked span prediction
; packet order prediction
; homologous traffic prediction
; transformer models
; traffic generation
; anomaly detection
; hexadecimal encoding
; bidirectional modeling
; traffic flow representation
; pre-training objectives
; encoder-decoder models
; fine-tuning strategy
; cyber threat detection
Copyright: This open access article is published under a Creative Commons CC BY 4.0 license, which permit the free download, distribution, and reuse, provided that the author and preprint are cited in any reuse.
