HU Xuntao,MENG Xiancai,LIU Shaoqing,et al.A Multi-Scale Pure Convolutional Neural Network based on Feature Engineering for Traffic Anomaly Prediction[J].Journal of Chengdu University of Information Technology,2025,40(05):589-593.[doi:10.16836/j.cnki.jcuit.2025.05.003]
基于特征工程的多尺度纯卷积神经网络的流量异常预测方法
- Title:
- A Multi-Scale Pure Convolutional Neural Network based on Feature Engineering for Traffic Anomaly Prediction
- 文章编号:
- 2096-1618(2025)05-0589-05
- Keywords:
- intrusion detection; deep learning; cybersecurity; sample less prediction; feature engineering
- 分类号:
- TP393.08
- 文献标志码:
- A
- 摘要:
- 入侵检测作为当前网络安全研究方向的焦点,其检测的准确性对于安全防护具有重要意义。针对传统机器学习与深度学习在预测准确率、模型稳定性以及多维数据中关键特征提取方面存在的问题,提出一种基于特征工程的多尺度纯卷积神经网络预测方法(FM-ConvNeXt)。在数据的处理上运用提出的特征工程方法,将遗传算法和主成分分析算法有效结合,提取关键特征,采用少数类过采样技术平衡数据样本,避免因样本不平衡导致的模型偏向性问题。在模型上以纯卷积神经网络(ConvNeXt)为主干模型,结合分流自注意力机制对特征进行高效学习,并添加全局响应归一化层用于减少模型内部协变量转移、增强模型稳定性。该方法在公开数据集CICIDS2018上预测的准确率为96.66%,F1-Score为96.63%,结果表明模型具有优异的预测能力,对于提高预测网络攻击的准确率和避免造成隐私数据泄露具有重要作用。
- Abstract:
- Intrusion detection,as a focus of current network security research,is of great significance to security protection in terms of its detection accuracy.Aiming at the problems of traditional machine learning and deep learning in prediction accuracy,model stability,and key feature extraction in multi-dimensional data.In this paper,a multi-scale pure convolutional neural network prediction method(FM-ConvNeXt)based on feature engineering is proposed.The proposed feature engineering method is applied in the processing of data,which effectively combines the genetic algorithm and principal component analysis algorithm to extract key features,and the minority class oversampling technique is used to balance the data samples to avoid the problem of model bias caused by sample imbalance.A pure convolutional neural network(ConvNeXt)is used as the backbone model in the model,combined with the shunt self-attention mechanism for efficient feature learning,and a global response normalization layer is added to reduce covariate transfer within the model and enhance model stability.The method predicts 96.66% accuracy and 96.63% F1-Score on the public dataset CICIDS-2018,and the results show that the model has excellent prediction ability,which is important for improving the accuracy of predicting cyberattacks and avoiding causing privacy data leakage.
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备注/Memo
收稿日期:2024-03-25
基金项目:国家自然科学基金资助项目(12105135); 合肥综合性国家科学中心能源研究院资助项目(21KZS208); 高校协同创新资助项目(GXXT-2022-003)
通信作者:刘少清.E-mail:liushaoqing@ie.ah.cn