Timestamp-Aware Spatio-Temporal Graph Contrastive Learning for Network Intrusion
arXiv:2603.22365v1 Announce Type: cross Abstract: With the rapid growth of interconnected devices, accurately detecting malicious activities in network traffic has become
arXiv:2608.17352v1 Announce Type: new Abstract: Distributed Denial-of-Service (DDoS) attacks threaten network availability, requiring a cognitive detection process that senses
arXiv:2606.28134v1 Announce Type: cross Abstract: Graph-based fraud detection is essential for safeguarding large-scale transaction systems, where undetected anomalies may lead to
arXiv:2602.05817v2 Announce Type: cross Abstract: The rapid expansion of Internet of Things (IoT) ecosystems has led to increasingly complex and heterogeneous network topologies.
arXiv:2604.08553v1 Announce Type: cross Abstract: Large Language Models (LLMs) have shown strong performance on text-attributed graphs (TAGs) due to their superior semantic
arXiv:2605.00482v1 Announce Type: cross Abstract: Mobile network operators must monitor thousands of heterogeneous network elements across the radio access network and the packet
arXiv:2610.24516v1 Announce Type: new Abstract: Spatial-temporal forecasting is pivotal for intelligent urban management yet remains challenging due to the intricate coupling of
arXiv:2604.18765v1 Announce Type: cross Abstract: Fault detection and diagnosis are critical for the optimal and safe operation of industrial processes. The correlations among
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