INTARG generates effective real-time adversarial attacks on time-series regression models by selectively targeting high-confidence high-error steps in a bounded-buffer online setting, increasing prediction error up to 2.42x while attacking under 10% of timesteps.
Forecasting network traffic: A survey and tutorial with open-source comparative evaluation
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A masked graph autoencoder on heterogeneous bidirectional graphs predicts per-flow NetFlow attachments and features from sliding windows of network traffic.
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INTARG: Informed Real-Time Adversarial Attack Generation for Time-Series Regression
INTARG generates effective real-time adversarial attacks on time-series regression models by selectively targeting high-confidence high-error steps in a bounded-buffer online setting, increasing prediction error up to 2.42x while attacking under 10% of timesteps.
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Forecasting Individual NetFlows using a Predictive Masked Graph Autoencoder
A masked graph autoencoder on heterogeneous bidirectional graphs predicts per-flow NetFlow attachments and features from sliding windows of network traffic.