2020
DOI: 10.1109/taes.2019.2961824
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Intrusion Detection System for the MIL-STD-1553 Communication Bus

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Cited by 22 publications
(16 citation statements)
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“…The context-based anomaly detection mechanism demonstrated perfect results (all anomalous messages were detected with zero false alarms) for both normal and abnormal scenarios when evaluated using datasets collected from our two testbeds and the dataset used by Stan et al [31]. In addition, we demonstrated the ability of the anomaly explanation engine to accurately explain the anomalies.…”
Section: Introductionmentioning
confidence: 63%
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“…The context-based anomaly detection mechanism demonstrated perfect results (all anomalous messages were detected with zero false alarms) for both normal and abnormal scenarios when evaluated using datasets collected from our two testbeds and the dataset used by Stan et al [31]. In addition, we demonstrated the ability of the anomaly explanation engine to accurately explain the anomalies.…”
Section: Introductionmentioning
confidence: 63%
“…Onodueze et al [28] obtained poor results when evaluating different classification methods, since the dataset used for training was highly imbalanced (this is known to cause most classification algorithms to fail or produce poor results); this dataset was collected from a realistic 1553 simulator. In contrast, Stan et al [31], who suggested using an unsupervised method, obtained better results by using Markov chains. For evaluation, they set up a real 1553 hardware-based testbed containing one BC and two RTs.…”
Section: Related Workmentioning
confidence: 92%
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