DeepStream: autoencoder-based stream temporal clustering and anomaly detection

Shimon Harush, Yair Meidan, Asaf Shabtai

Computers & Security 106, 102276, 2021

The increasing number of IoT devices in “smart” environments, such as homes, offices, and cities, produce seemingly endless data streams and drive many daily decisions. Consequently, there is growing interest in identifying contextual information from sensor data to facilitate the performance of various tasks, e.g., traffic management, cyber attack detection, and healthcare monitoring. The correct identification of contexts in data streams is helpful for many tasks, for example, it can assist in providing high-quality recommendations to end users and in reporting anomalous behavior based on the detection of unusual contexts. This paper presents DeepStream, a novel data stream temporal clustering algorithm that dynamically detects sequential and overlapping clusters. DeepStream is tuned to classify contextual information in real time and is capable of coping with a high-dimensional feature space. DeepStream …