  Existing cyber security solutions have been basically developed using
knowledge-based models that often cannot trigger new cyber-attack families.
With the boom of Artificial Intelligence (AI), especially Deep Learning (DL)
algorithms, those security solutions have been plugged-in with AI models to
discover, trace, mitigate or respond to incidents of new security events. The
algorithms demand a large number of heterogeneous data sources to train and
validate new security systems. This paper presents the description of new
datasets, the so-called ToN_IoT, which involve federated data sources collected
from telemetry datasets of IoT services, operating system datasets of Windows
and Linux, and datasets of network traffic. The paper introduces the testbed
and description of TON_IoT datasets for Windows operating systems. The testbed
was implemented in three layers: edge, fog and cloud. The edge layer involves
IoT and network devices, the fog layer contains virtual machines and gateways,
and the cloud layer involves cloud services, such as data analytics, linked to
the other two layers. These layers were dynamically managed using the platforms
of software-Defined Network (SDN) and Network-Function Virtualization (NFV)
using the VMware NSX and vCloud NFV platform. The Windows datasets were
collected from audit traces of memories, processors, networks, processes and
hard disks. The datasets would be used to evaluate various AI-based cyber
security solutions, including intrusion detection, threat intelligence and
hunting, privacy preservation and digital forensics. This is because the
datasets have a wide range of recent normal and attack features and
observations, as well as authentic ground truth events. The datasets can be
publicly accessed from this link [1].
