Consumer Internet of things research often involves collecting network traffic sent or
received by IoT devices. These data are typically collected via crowdsourcing or while …

In this chapter, we use K-means clustering to analyze various relationships between
malware samples. We consider a dataset comprising 20 malware families with 1000 …

In recent years, there has been growing recognition that machine learning is not a “silver
bullet” that can magically detect all cyber‐security threats, but is instead more effective in a …

We present a new machine learning-based attack that exploits network patterns to detect the
presence of smart IoT devices and running services in the WiFi radio spectrum. We perform …

Abstract Machine learning (ML) has been extensively used in Internet of Things (IoT)
applications, including traffic profiling, network security, and IoT device identification …

Identifying threats in encrypted network traffic without compromising the integrity of the data
is an important issue. Many IoT devices communicate with the cloud to store and process …

With the rapid development of the IoT, smart home plays an increasingly important role in
daily life. At the same time, privacy issues have gradually aroused the concern of …

The growing Internet of Things (IoT) introduces new security challenges for network activity
monitoring. Most IoT devices are vulnerable because of a lack of security awareness from …

The rapid development of the Internet of Things (IoT) has brought challenges to IoT
platforms for high-efficiency deployments and low-budget management. Identifying IoT …

Millions of Android applications are now deployed on billions of smartphones and tablet
devices. An enormous amount of users' private data are being collected and made …