The significant advancements in sensors and other resource-constrained devices, capable
of collecting data and communicating wirelessly, are poised to revolutionize numerous …

Deep autoencoder has been extensively used for anomaly detection. Training on the normal
data, the autoencoder is expected to produce higher reconstruction error for the abnormal …

With the development of the Internet of Things (IoT) technology, intrusion detection has
become a key technology that provides solid protection for IoT devices from network …

The analysis of a substantial portion of network data is a requirement for almost any
machine learning-based network intrusion detection method. High dimension features, a …

Recently, anomaly scores have been formulated using reconstruction loss of the
adversarially learned generators and/or classification loss of discriminators. Unavailability of …

Anomaly detection based on generative models usually uses the reconstruction loss of
samples for anomaly discrimination. However, there are two problems in semi-supervised or …

To facilitate offline reinforcement learning, uncertainty estimation is commonly used to detect
out-of-distribution data. By inspecting, we show that current explicit uncertainty estimators …

Density models are fundamental in machine learning and have received a widespread
application in practical cognitive modeling tasks and learning problems. In this work, we …

Federated learning is a machine learning framework proposed in recent years. In horizontal
federated learning, multiple participants cooperate to train and obtain a common final model …

Artificial Intelligence of Things (AIoT) is an innovative paradigm expected to enable various
consumer applications that is transforming our lives. While enjoying benefits and services …