Enormous amounts of data are being produced everyday by sub-meters and smart sensors
installed in residential buildings. If leveraged properly, that data could assist end-users …

Nowadays, reliability assurance is crucial in components of IT infrastructures. Unavailability
of any element or connection results in downtime and triggers monetary and performance …

Log anomaly detection on edge devices is the key to enhance edge security when
deploying IoT systems. Despite the success of many newly proposed deep learning based …

Abstract Semantics-aware anomaly detection based on log has attracted much attention.
However, the existing methods based on the weighted aggregation of all word vectors might …

Intrusion Detection Systems (IDS) serve as critical components in safeguarding network
security by detecting malicious activities. Although IDS has recently been treated primarily …

Deep support vector data description (DeSVDD) is an emerging anomaly detection method
based on the deep learning methodology. However, few studies take the confidence of …

Wireless sensing builds upon machine learning shows encouraging results. However,
adopting wireless sensing as a large-scale solution remains challenging as experiences …

With the continuous development of artificial intelligence and big data, more and more
communication protocols have appeared in the network connection. Because the network …

The use of anomaly detection for log monitoring requires parsing model input features from
raw, unstructured data. Log parsing methods come in many forms, but are generally …

There are a large number of end devices in an IoT system, which may malfunction due to
various reasons, such as being attacked. Anomaly detection of the devices and the whole …