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Omni SCADA Intrusion Detection Using Deep Learning Algorithms
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DOI：10.1109/JIOT.2020.3009180.png)
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摘要
En 中文
In this article, we investigate deep-learning-based omni intrusion detection system (IDS) for supervisory control and data acquisition (SCADA) networks that are capable of detecting both temporally uncorrelated and correlated attacks. Regarding the IDSs developed in this article, a feedforward neural network (FNN) can detect temporally uncorrelated attacks at an F1 of 99.9670.005 but correlated attacks as low as 582. In contrast, long short-term memory (LSTM) detects correlated attacks at 99.560.01 while uncorrelated attacks at 99.30.1. Combining LSTM and FNN through an ensemble approach further improves the IDS performance with F1 of 99.680.04 regardless the temporal correlations among the data packets.
Keyword:
Feature extraction
IP networks
Registers
Machine learning
Intrusion detection
Software
Denial of Service (DoS)
feedforward neural networks (FNNs)
intrusion detection
intrusion detection system (IDS)
long-short term memory (LSTM)
Modbus
multilayer perceptron
network security
supervised learning
supervisory control and data acquisition (SCADA) systems
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期刊
IF:
8.9
论文数:
1.4W
被引数:
7.8W
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引用论文
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