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.

本文针对现有网络攻击检测与识别模型存在的鲁棒性不足、特征不稳定和数据噪声干扰的问题，提出了一种基于时间谱的攻击流量检测与识别方法。通过滑动窗口对流量数据进行分段，构建特征序列，并运用新提出的谱标签生成方法，将标签序列转化为谱标签，进而提高了模型的识别准确性和鲁棒性，尤其在噪声环境中表现出色。

基于时间谱的攻击流量识别方法