用量子模型提升钓鱼网址识别准确率，最高达0.89
PhishVQC: Optimizing Phishing URL Detection with Correlation Based Feature Selection and Variational Quantum Classifier
- 结合量子特征映射与变分量子电路，构建新型检测模型
- 在多组实验中实现最高0.89的宏平均F1分数，提升22%
- 适合关注量子机器学习在网络安全应用的研究者
钓鱼网址检测对网络安全至关重要，因恶意网站常伪装以窃取敏感信息。传统机器学习在复杂真实场景中表现受限，主要受大规模数据集和复杂模式影响。本文受量子计算启发，提出使用变分量子分类器（VQC）提升钓鱼网址检测能力。我们构建了PhishVQC模型，融合量子特征映射与变分回路（如RealAmplitude和EfficientSU2）。在不同数据规模与特征映射重复次数的两组实验中评估，该模型最高达到0.89的宏平均F1分数，相比先前研究提升22%。结果表明量子机器学习在提高检测精度方面具有潜力。同时研究指出，随着数据集增大，运行耗时显著增加，存在计算挑战。
原文摘要 · Abstract (English)
Phishing URL detection is crucial in cybersecurity as malicious websites disguise themselves to steal sensitive infor mation. Traditional machine learning techniques struggle to per form well in complex real-world scenarios due to large datasets and intricate patterns. Motivated by quantum computing, this paper proposes using Variational Quantum Classifiers (VQC) to enhance phishing URL detection. We present PhishVQC, a quantum model that combines quantum feature maps and vari ational ansatzes such as RealAmplitude and EfficientSU2. The model is evaluated across two experimental setups with varying dataset sizes and feature map repetitions. PhishVQC achieves a maximum macro average F1-score of 0.89, showing a 22% improvement over prior studies. This highlights the potential of quantum machine learning to improve phishing detection accuracy. The study also notes computational challenges, with execution wall times increasing as dataset size grows.
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