Abstract:

Distributed Denial of Service （DDoS） attacks remain one of the primary threats faced by modern networks due to their high stealthiness and polymorphism. With the development of the Internet of Things （IoT） and cloud computing， DDoS traffic exhibits characteristics such as suddenness， heterogeneity， and rapid evolution， posing challenges for traditional detection methods in complex attack identification and sample imbalance. To overcome these limitations， a DDoS detection method combining DeepInsight and Classical-to-Quantum Convolutional Neural Network （DI-C2Q-CNN） was proposed. First， DeepInsight technology was utilized to map multi-dimensional time-series network traffic data into structured grayscale images intelligently， and by reconstructing spatial relationships between features explicitly， the identifiability of attack patterns was enhanced. Second， a Classical-to-Quantum Convolutional Neural Network （C2Q-CNN） hybrid architecture was constructed， integrating classical Convolutional Neural Network （CNN） with quantum convolution modules. By leveraging the representational capability of quantum superposition states and high-dimensional feature mapping， the higher-order representation of image features and the ability to learn complex patterns were enhanced， thereby improving the capability to identify complex network attack patterns. Additionally， to address training bottlenecks caused by small samples and class imbalance， multiple transfer learning solutions were introduced to enhance model adaptability and robustness. Experimental results demonstrate that the proposed method outperforms mainstream models such as Support Vector Machine （SVM） and CNN-LSTM on the UNSW-NB15 dataset， even under extreme sample compression and class imbalance conditions， showing good performance and application potential in DDoS attack detection tasks.

Key words: Distributed Denial of Service (DDoS), DeepInsight, Quantum Convolutional Neural Network (QCNN), transfer learning, class imbalance

摘要：

分布式拒绝服务（DDoS）攻击因隐蔽性强、形式多变，仍是现代网络面临的主要威胁之一。随着物联网与云计算的发展，DDoS流量呈现突发性、异构性和快速演化等特点，导致传统检测方法在复杂攻击识别与样本不均衡等方面面临挑战。为了克服这些局限性，提出一种基于DeepInsight与经典?量子卷积神经网络（DI-C2Q-CNN）的DDoS攻击检测方法。首先，利用DeepInsight技术将多维时间序列网络流量数据智能映射为结构化灰度图像，通过显式重构特征间的空间关系，有助于提升攻击模式的可识别性。其次，构建融合经典卷积神经网络（CNN）与量子卷积模块的经典?量子卷积神经网络（C2Q-CNN）混合架构，结合量子叠加态的表征能力与高维特征映射能力，增强对图像特征的高阶表征以及复杂模式的学习能力，从而提升识别复杂网络攻击模式的能力。此外，为了缓解小样本和类别不平衡造成的训练瓶颈，引入迁移学习策略，提高模型的适应性和鲁棒性。实验结果表明，所提方法在UNSW-NB15数据集上即使在极端样本压缩和类别失衡条件下，仍优于支持向量机（SVM）和CNN-LSTM等主流模型，在DDoS攻击检测任务中展现出良好的性能与应用潜力。

关键词: 分布式拒绝服务, DeepInsight, 量子卷积神经网络, 迁移学习, 类别失衡