下一代行動網路在關鍵任務上高度依賴機器學習，因此模型對於對抗性攻擊的穩健性至關重要。我們提出了 AdvTabDDPM，一個基於擴散模型的框架，用於混合型表格資料中生成邊界敏感的潛在對抗性樣本。透過定義動態邊界區域並在對抗訓練中引入標籤不確定性（label uncertainty），我們的方法能精確針對低信心區域，促使決策邊界更加平滑且可靠。實驗結果顯示，AdvTabDDPM 能將生成的樣本集中於決策邊界附近，並將整體準確率從 82.06% 提升至 92.57%，在所有評估方法中最接近未受擾動資料的準確率 93.87%，並優於 FGSM、PGD 與 CW。這些結果證明，結合邊界導向的樣本生成與標籤不確定性，能為網路機器學習模型提供有效的穩健性與準確性提升策略。;Next-generation mobile networks rely on machine learning for critical tasks, making model robustness against adversarial attacks essential. We propose AdvTabDDPM, a diffusion-based framework that generates boundary-sensitive potential adversarial examples in mixed-type tabular datasets. By defining a dynamic boundary zone and incorporating label uncertainty during adversarial training, our method precisely targets low-confidence regions, encouraging smoother and more reliable decision boundaries. Experiments show that AdvTabDDPM concentrates examples near decision boundaries and improves overall accuracy from 82.06% to 92.57%, the closest among evaluated methods to the clean data accuracy of 93.87%, outperforming FGSM, PGD, and CW. These results demonstrate that combining boundary-guided generation with label uncertainty provides an effective strategy for robust and accurate network ML models.
