Abstract: An anomaly detection method for detecting an anomaly in an in-vehicle network of an in-vehicle network system including a plurality of electronic control units that transmit and receive messages via the network includes: generating image data of a reception interval between a plurality of messages included in a message sequence in a predetermined period out of a message sequence received from the in-vehicle network, or image data of a transition of a sensor value of the plurality of messages; classifying the image data using a trained CNN according to whether an attack message has been inserted in the predetermined period; and when the attack message has been inserted in the predetermined period, outputting a detection result indicating that an insertion attack which is an insertion of the attack message has been made in the predetermined period.

Abstract: A predictive maintenance server receives data from sensors of equipment. The server uses one or more machine learning models to assign an anomaly score. Responsive to the anomaly score exceeding a threshold value, the server may issue an alert. The machine learning model may be supervised or unsupervised. In one embodiment, the machine learning model use several sensor channels to predict the values of one or more vitals of the equipment and compare the predicted values to the actual measured values of the vitals. The server may assign an anomaly score based on the differences between the predicted values and the measured values. In one embodiment, the machine learning model may be an autoencoder that generates a distribution of the measurement values to determine the likelihood of observing the actual measured values in a normal operation. In one embodiment, the server may use a histogram approach to predict anomaly.

Abstract: A predictive maintenance server receives data from sensors of equipment. The server uses one or more machine learning models to assign an anomaly score. Responsive to the anomaly score exceeding a threshold value, the server may issue an alert. The machine learning model may be supervised or unsupervised. In one embodiment, the machine learning model use several sensor channels to predict the values of one or more vitals of the equipment and compare the predicted values to the actual measured values of the vitals. The server may assign an anomaly score based on the differences between the predicted values and the measured values. In one embodiment, the machine learning model may be an autoencoder that generates a distribution of the measurement values to determine the likelihood of observing the actual measured values in a normal operation. In one embodiment, the server may use a histogram approach to predict anomaly.