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蔡赛华
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[1] CT-SSSA: Malicious traffic augmentation based on classifier transGAN and spatial-channel synergistic self-attention
[2] APT-ATT: An efficient APT attribution model based on heterogeneous threat intelligence representation and CTGAN
[3] CDDA-MD: An efficient malicious traffic detection method based on concept drift detection and adaptation technique
[4] DMSE: An efficient malicious traffic detection model based on deep multi-stacking ensemble learning
[5] GSA-DT: A Malicious Traffic Detection Model Based on Graph Self-Attention Network and Decision Tree
[6] MTD-FRD: Malicious traffic detection method based on feature representation and conditional diffusion model
[7] DDP-DAR: Network intrusion detection based on denoising diffusion probabilistic model and dual-attention residual network
[8] A malicious network traffic detection model based on bidirectional temporal convolutional network with multi-head self-attention mechanism
[9] CD-BTMSE: A Concept Drift detection model based on Bidirectional Temporal Convolutional Network and Multi-Stacking Ensemble learning
[10] Minimal Rare Pattern-Based Outlier Detection Approach For Uncertain Data Streams Under Monotonic Constraints
[11] An Empirical Study on Vulnerability Detection for Source Code Software based on Deep Learning
[12] A Efficient Network Traffic Classification Method based on Combined Feature Dimensionality Reduction
[13] An Identification Algorithm of Attacking Programs based on Quadratic Feature Selection and Fast Decision Tree
[14] MWFP-outlier: Maximal weighted frequent-pattern-based approach for detecting outliers from uncertain weighted data streams
[15] An Efficient Outlier Detection Approach for Streaming Sensor Data Based on Neighbor Difference and Clustering
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