研究代码漏洞检测中数据窥探对模型性能的影响，发现其影响有限。
Impact of Data Snooping on Deep Learning Models for Locating Vulnerabilities in Lifted Code
- 用含训练样本的數據訓練嵌入模型，模擬數據窺探現象。
- 模型性能未明顯下降，說明數據窺探影響小或隨機丟棄樣本含關鍵特徵。
- GPT-2嵌入始終優於其他方法，顯示其對複雜代碼特徵的強健表達能力。
本研究探討神經網絡在檢測提升代碼漏洞時，數據窺探對模型性能的影響。此前研究已使用 word2vec 及單向、雙向 Transformer 嵌入。本研究重點分析嵌入模型訓練數據包含神經網絡訓練與驗證樣本時的情況。結果顯示，引入數據窺探並未顯著改變模型表現，暗示數據窺探影響極小，或隨機丟棄的樣本中隱藏了實現最佳性能的關鍵特徵。此外，研究結果支持以往發現：使用 GPT-2 嵌入的模型持續優於其他嵌入方式。即使在引入數據窺探的情況下，該優勢仍成立，表明 GPT-2 在不理想條件下仍能穩健捕捉複雜代碼特徵。
原文摘要 · Abstract (English)
This study examines the impact of data snooping on neural networks used to detect vulnerabilities in lifted code, and builds on previous research that used word2vec and unidirectional and bidirectional transformer-based embeddings. The research specifically focuses on how model performance is affected when embedding models are trained with datasets, which include samples used for neural network training and validation. The results show that introducing data snooping did not significantly alter model performance, suggesting that data snooping had a minimal impact or that samples randomly dropped as part of the methodology contained hidden features critical to achieving optimal performance. In addition, the findings reinforce the conclusions of previous research, which found that models trained with GPT-2 embeddings consistently outperformed neural networks trained with other embeddings. The fact that this holds even when data snooping is introduced into the embedding model indicates GPT-2's robustness in representing complex code features, even under less-than-ideal conditions.
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