To address the issues of insufficient robustness, unstable features, and data noise interference in existing network attack detection and identification models, this paper proposes an attack traffic detection and identification method based on temporal spectrum. First, traffic data is segmented by a sliding window to construct a feature sequence and a corresponding label sequence for network traffic. Next, the proposed spectral label generation methods, SSPE and COAP, are applied to transform the label sequence into spectral labels and the feature sequence into temporal features. Spectral labels and temporal features are used to capture and represent behavioral patterns of attacks. Finally, the constructed temporal features and spectral labels are used to train models, which subsequently detects and identifies network attack behaviors. Experimental results demonstrate that compared to traditional methods, models trained with the SSPE or COAP method improve identification accuracy by 10%, and exhibit strong robustness, particularly in noisy environments.

翻译：针对现有网络攻击检测与识别模型鲁棒性不足、特征不稳定及数据噪声干扰等问题，本文提出一种基于时序谱的攻击流量检测与识别方法。首先，通过滑动窗口对流量数据进行分段，构建网络流量的特征序列及对应的标签序列。随后，应用所提出的谱标签生成方法SSPE与COAP，将标签序列转化为谱标签，并将特征序列转化为时序特征。谱标签与时序特征用于捕捉并表征攻击行为模式。最后，利用构建的时序特征与谱标签训练模型，进而实现网络攻击行为的检测与识别。实验结果表明，与传统方法相比，采用SSPE或COAP方法训练的模型在识别准确率上提升10%，且表现出较强的鲁棒性，尤其在噪声环境下表现突出。