量子自编码器在网络安全中的异常检测
Quantum Autoencoders for Anomaly Detection in Cybersecurity
AI总结：
本研究将量子自编码器应用于网络安全异常检测，在BETH数据集上，8特征QAE在数据有限时F1达0.87，优于经典自编码器的0.77，展示了量子优势。
摘要：
网络安全中的异常检测是一项具有挑战性的任务，其中正常事件的数量远远超过异常事件，且新的异常频繁出现。经典自编码器已被用于异常检测，但在数据有限的场景中表现不佳，而量子对应方法可能克服这一局限。在本工作中，我们将量子自编码器（QAEs）应用于网络安全中的异常检测，具体针对BPF扩展跟踪蜜罐（BETH）数据集。QAEs在多种编码技术、拟设类型、重复次数和特征选择策略下进行了评估。我们的结果表明，使用Dense-Angle编码和RealAmplitude拟设的8特征QAE，即使在训练样本大幅减少的情况下，也能优于经典自编码器（CAEs）。我们展示并讨论了量子编码和特征选择对开发量子模型的影响。在数据有限的设置中，性能最佳的QAE模型的F1分数为0.87，优于CAE的0.77。这些发现表明，QAEs可能在数据有限的场景中为异常检测提供实际优势。
英文摘要：
Anomaly detection in cybersecurity is a challenging task, where normal events far outnumber anomalous ones with new anomalies occurring frequently. Classical autoencoders have been used for anomaly detection, but struggles in data-limited settings which quantum counterparts can potentially overcome. In this work, we apply Quantum Autoencoders (QAEs) for anomaly detection in cybersecurity, specifically on the BPF-extended tracking honeypot (BETH) dataset. QAEs are evaluated across multiple encoding techniques, ansatz types, repetitions, and feature selection strategies. Our results demonstrate that an 8-feature QAE using Dense-Angle encoding with a RealAmplitude ansatz can outperform Classical Autoencoders (CAEs), even when trained on substantially fewer samples. The effects of quantum encoding and feature selection for developing quantum models are demonstrated and discussed. In a data-limited setting, the best performing QAE model has a F1 score of 0.87, better than that of CAE (0.77). These findings suggest that QAEs may offer practical advantages for anomaly detection in data-limited scenarios.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
