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- W4320029316 abstract "The legacy design of the Controller Area Network (CAN) weakens the encryption and authentication of the In-Vehicle Networks (IVN). Anomaly detection systems, e.g. the Long-Short Term Memory (LSTM) based Intrusion Detection System (IDS), are employed to remedy the defection of CAN. Existing works feed the LSTM-based IDS with the byte values of the data payload of CAN to train and test the LSTM model. In this paper, we propose an LSTM-based IDS leveraging byte-level features, i.e., byte flip rate, byte-level change rage, and byte-level distinct value rate, to augment the sensitivity of proposed LSTM-based IDS when distinguishing malicious CAN messages. By using the byte-level signal features, the proposed system achieves high accuracy with a small size of the training dataset. The experiment results show that the model with the byte-level features can achieve a performance gain of the <tex xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>$F$</tex> <inf xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>1</inf> Score up to 20% over the model without the byte-level features." @default.
- W4320029316 created "2023-02-12" @default.
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- W4320029316 date "2022-12-04" @default.
- W4320029316 modified "2023-10-06" @default.
- W4320029316 title "Leveraging Byte-Level Features for LSTM-based Anomaly Detection in Controller Area Networks" @default.
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- W4320029316 doi "https://doi.org/10.1109/globecom48099.2022.10000642" @default.
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