An FNN-LSTM ensemble detects both temporally correlated and uncorrelated attacks in a simulated SCADA network with a reported macro F1 of 99.68%, but the evaluation splits time-series data randomly, which risks inflated accuracy.
Hierarchical Online Intrusion Detection for SCADA Networks
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abstract
We propose a novel hierarchical online intrusion detection system (HOIDS) for supervisory control and data acquisition (SCADA) networks based on machine learning algorithms. By utilizing the server-client topology while keeping clients distributed for global protection, high detection rate is achieved with minimum network impact. We implement accurate models of normal-abnormal binary detection and multi-attack identification based on logistic regression and quasi-Newton optimization algorithm using the Broyden-Fletcher-Goldfarb-Shanno approach. The detection system is capable of accelerating detection by information gain based feature selection or principle component analysis based dimension reduction. By evaluating our system using the KDD99 dataset and the industrial control system dataset, we demonstrate that HOIDS is highly scalable, efficient and cost effective for securing SCADA infrastructures.
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eess.SY 1years
2019 1verdicts
REJECT 1representative citing papers
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Omni SCADA Intrusion Detection Using Deep Learning Algorithms
An FNN-LSTM ensemble detects both temporally correlated and uncorrelated attacks in a simulated SCADA network with a reported macro F1 of 99.68%, but the evaluation splits time-series data randomly, which risks inflated accuracy.