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.

翻译：钓鱼URL检测在网络安全中至关重要，恶意网站通过伪装窃取敏感信息。传统机器学习技术因数据集庞大、模式复杂，在现实复杂场景中表现不佳。受量子计算启发，本文提出使用变分量子分类器（VQC）增强钓鱼URL检测。我们提出PhishVQC量子模型，该模型结合了量子特征映射与RealAmplitude、EfficientSU2等变分拟设。模型在两种不同数据集规模和特征映射重复次数的实验设置中进行评估。PhishVQC取得了0.89的最高宏平均F1分数，较先前研究提升22%。这凸显了量子机器学习在提升钓鱼检测准确率方面的潜力。研究同时指出计算挑战，即执行耗时随数据集规模增大而增加。