Abstract: An anomalous behavior detector has been designed to detect novel behavioral changes of devices based on network traffic data that likely correlate to anomalous behaviors. The anomalous behavior detector uses the local outlier factor (LOF) algorithm with novelty detection. After initial semi-supervised training with a single class training dataset representing stable device behaviors, the obtained model continues learning frontiers that delimit subspaces of inlier observations with live network traffic data. Instead of traffic variables being used as features, the features that form feature vectors are similarities of network traffic variable values across time intervals. A feature vector for the anomalous behavior detector represents stability or similarity of network traffic variables that have been chosen as device identifiers and behavioral indicators.

Abstract: An anomalous behavior detector has been designed to detect novel behavioral changes of devices based on network traffic data that likely correlate to anomalous behaviors. The anomalous behavior detector uses the local outlier factor (LOF) algorithm with novelty detection. After initial semi-supervised training with a single class training dataset representing stable device behaviors, the obtained model continues learning frontiers that delimit subspaces of inlier observations with live network traffic data. Instead of traffic variables being used as features, the features that form feature vectors are similarities of network traffic variable values across time intervals. A feature vector for the anomalous behavior detector represents stability or similarity of network traffic variables that have been chosen as device identifiers and behavioral indicators.

Abstract: An anomalous behavior detector has been designed to detect novel behavioral changes of devices based on network traffic data that likely correlate to anomalous behaviors. The anomalous behavior detector uses the local outlier factor (LOF) algorithm with novelty detection. After initial semi-supervised training with a single class training dataset representing stable device behaviors, the obtained model continues learning frontiers that delimit subspaces of inlier observations with live network traffic data. Instead of traffic variables being used as features, the features that form feature vectors are similarities of network traffic variable values across time intervals. A feature vector for the anomalous behavior detector represents stability or similarity of network traffic variables that have been chosen as device identifiers and behavioral indicators.

Abstract: Identifying Internet of Things (IoT) devices with packet flow behavior including by using machine learning models is disclosed. Information associated with a network communication of an IoT device is received. A determination of whether the IoT device has previously been classified is made. In response to determining that the IoT device has not previously been classified, a determination is made that a probability match for the IoT device against a behavior signature exceeds a threshold. The behavior signature includes at least one time series feature for an application used by the IoT device. Based at least in part on the probability match, a classification of the IoT device is provided to a security appliance configured to apply a policy to the IoT device.

Abstract: An embodiment discloses a lighting device, comprising a lamp holder, a lamp panel, a lamp base, wherein the lamp panel includes a light source and located in the lamp holder, the lamp holder is connected to the lamp base, a physical displacement control module, and a power supply driver module placed in the lamp base, wherein the physical displacement control module is connected to the power supply driver module, adapted to control the power supply driver module to change an output signal to the lamp panel according to detected physical displacement, and the power supply driver module affords power supply to the physical displacement control module.

Abstract: An embodiment discloses a lighting device, comprising a lamp holder, a lamp panel, a lamp base, wherein the lamp panel includes a light source and located in the lamp holder, the lamp holder is connected to the lamp base, a physical displacement control module, and a power supply driver module placed in the lamp base, wherein the physical displacement control module is connected to the power supply driver module, adapted to control the power supply driver module to change an output signal to the lamp panel according to detected physical displacement, and the power supply driver module affords power supply to the physical displacement control module.