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Enhancing Cyber Resilience in Smart Meter Networks Against Resource-Stealing Attacks Using Hybrid Attack Classification

Kalildass JANAKIRAMAN et al.

Economic Computation and Economic Cybernetics Studies and Research2026https://doi.org/10.24818/18423264/60.1.26.14article
ABDC C
Weight
0.50

What the paper says

The Advanced Metering Infrastructure (AMI) represents the cyber backbone of the smart grid, making it highly susceptible to various cyber threats.This research addresses key challenges in AMI transmission networks, including low detection accuracy, model over fitting, and scalability limitations.To mitigate these vulnerabilities, in the proposed model analyses the information to detect and classify instances of resource stealing or anomalous behaviour.The proposed model Ensemble 1D-MN3 employs an integrated 1D CNN and MobileNetV3 architecture with hyper parameter tuning for classification task, which is particularly effective at extracting meaningful one-dimensional features and capturing complex temporal relationships in time-series data.This results in more accurate and reliable detection outcomes.Overall, the implementation of the proposed framework provides a robust and scalable solution for anomaly detection in smart metre data, making it highly suitable for real-world deployment within AMI networks.

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https://doi.org/https://doi.org/10.24818/18423264/60.1.26.14

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@article{kalildass2026,
  title        = {{Enhancing Cyber Resilience in Smart Meter Networks Against Resource-Stealing Attacks Using Hybrid Attack Classification}},
  author       = {Kalildass JANAKIRAMAN et al.},
  journal      = {Economic Computation and Economic Cybernetics Studies and Research},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.24818/18423264/60.1.26.14},
}

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.