DDSCM: dual-scale depthwise separable convolution module for AMI-oriented network intrusion detection of the power consumption side

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Abstract
Advanced Metering Infrastructure (AMI) of the power consumption side, serving as the neural hub of modern power systems, orchestrates real time energy data flows between distributed smart meters and centralized grid control centers, yet this pervasive connectivity introduces critical vulnerabilities to cyber attacks. Traditional intrusion detection systems struggle with the high-dimensional traffic patterns and severe class imbalance inherent in AMI networks, particularly in identifying handful attack variants amidst overwhelming normal traffic. To address these challenges, this paper proposes a detection model incorporating Random Fourier Features (RFF), Adaptive Weighted Focal Loss (AWFL), and a dual-scale convolutional neural network. The model first employs RFF to construct a low-dimensional explicit mapping, replacing the implicit kernel function in kernel PCA. This transforms the original nonlinear eigenvalue problem into linear PCA on random features, thereby reducing computational complexity significantly while preserving the capability of nonlinear dimensionality reduction. Subsequently, AWFL dynamically adjusts the model’s focus on different class samples during training, significantly improving class imbalance handling in network intrusion datasets. Finally, a dual-scale depthwise separable convolution module (DDSCM) is adopted to extract spatial features from input information, enabling more accurate identification of intrusion types. Evaluations on the NSL-KDD dataset and TON_IoT dataset demonstrate that the proposed model achieves superior intrusion detection performance, outperforming various comparative models in multiclass classification tasks regarding accuracy, precision, recall and F1-score.
Funding Information
  • National Natural Science Foundation of China (62163036)
  • Yunnan Province “Xingdian Talents Support Plan” Industrial Innovation Talents (202105AC160094)
  • Project for Reserve Talents of Young and Middle-aged Academic and Technical Leaders in Yunnan Province (202105AC160094)

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