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蔡赛华
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[1] APT-ATT: An efficient APT attribution model based on heterogeneous threat intelligence representation and CTGAN
[2] DDP-DAR: Network intrusion detection based on denoising diffusion probabilistic model and dual-attention residual network
[3] CDDA-MD: An efficient malicious traffic detection method based on concept drift detection and adaptation technique
[4] GSA-DT: A Malicious Traffic Detection Model Based on Graph Self-Attention Network and Decision Tree
[5] MTD-FRD: Malicious traffic detection method based on feature representation and conditional diffusion model
[6] DMSE: An efficient malicious traffic detection model based on deep multi-stacking ensemble learning
[7] CT-SSSA: Malicious traffic augmentation based on classifier transGAN and spatial-channel synergistic self-attention
[8] CD-BTMSE: A Concept Drift detection model based on Bidirectional Temporal Convolutional Network and Multi-Stacking Ensemble learning
[9] A malicious network traffic detection model based on bidirectional temporal convolutional network with multi-head self-attention mechanism
[10] MWFP-outlier: Maximal weighted frequent-pattern-based approach for detecting outliers from uncertain weighted data streams
[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] 数据分析与挖掘课程教学改革研究
[14] An Identification Algorithm of Attacking Programs based on Quadratic Feature Selection and Fast Decision Tree
[15] An Efficient Network Traffic Classification Method based on Combined Feature Dimensionality Reduction
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