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Existing cyber security solutions have been basically developed usingknowledge-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 todiscover, trace, mitigate or respond to incidents of new security events. Thealgorithms demand a large number of heterogeneous data sources to train andvalidate new security systems. This paper presents the description of newdatasets, the so-called ToN_IoT, which involve federated data sources collectedfrom telemetry datasets of IoT services, operating system datasets of Windowsand Linux, and datasets of network traffic. The paper introduces the testbedand description of TON_IoT datasets for Windows operating systems. The testbedwas implemented in three layers: edge, fog and cloud. The edge layer involvesIoT and network devices, the fog layer contains virtual machines and gateways,and the cloud layer involves cloud services, such as data analytics, linked tothe other two layers. These layers were dynamically managed using the platformsof software-Defined Network (SDN) and Network-Function Virtualization (NFV)using the VMware NSX and vCloud NFV platform. The Windows datasets werecollected from audit traces of memories, processors, networks, processes andhard disks. The datasets would be used to evaluate various AI-based cybersecurity solutions, including intrusion detection, threat intelligence andhunting, privacy preservation and digital forensics. This is because thedatasets have a wide range of recent normal and attack features andobservations, as well as authentic ground truth events. The datasets can bepublicly accessed from this link [1].

Federated TON_IoT Windows Datasets for Evaluating AI-based Security Applications