Abstract Number of connected devices is steadily increasing and these devices continuously
generate data streams. Real-time processing of data streams is arousing interest despite …

Android platform has dominated the markets of smart mobile devices in recent years. The
number of Android applications (apps) has seen a massive surge. Unsurprisingly, Android …

Detecting changes in multidimensional data streams is an important and challenging task. In
unsupervised change detection, changes are usually detected by comparing the distribution …

Situational awareness tries to grasp the important events and circumstances in the physical
world through sensing, communication, and reasoning. Tracking the evolution of changing …

In this work, we propose a novel framework of autonomic intrusion detection that fulfills
online and adaptive intrusion detection over unlabeled HTTP traffic streams in computer …

Many Android developers fail to properly implement SSL/TLS during the development of an
app, which may result in Man-In-The-Middle (MITM) attacks or phishing attacks. In this work …

Anomaly intrusion detection in big data environments calls for lightweight models that are
able to achieve real-time performance during detection. Abstracting audit data provides a …

In an era of ubiquitous large-scale evolving data streams, data stream clustering (DSC) has
received lots of attention because the scale of the data streams far exceeds the ability of …

Clustering streams of data is of increasing importance in many applications. In this paper,
we propose a new synchronization-based clustering approach for evolving data streams …

Stream clustering is a fundamental problem in many streaming data analysis applications.
Comparing to classical batch-mode clustering, there are two key challenges in stream …