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Disclaimer:
This Resource is offered and provided outside of the IMPACT mediation framework. IMPACT and the IMPACT Coordination Council/Blackfire Technology, Inc. expressly disclaim all conditions, representations and warranties including but not limited to Resource availability, quality, accuracy, non-infringement, and non-interference. All Resource information and access is controlled by entities and under terms that are external to the IMPACT legal framework.
Summary
DS-1258
Sherlock
External Dataset
External Data Source
BGU Cyber Security Research Center
Unknown
Unknown
56 (lowest rank is 56)
Description
A labeled dataset with billions of records covering a wide variety of low-privileged monitorable smartphone features collected from 50 volunteers over a few years. The labels were created by having the volunteers run applications infected with malware -based on real malwares found in the wild.
The dataset is essentially a massive time-series dataset spanning nearly every single kind of software and hardware sensor that can be sampled from a Samsung Galaxy S5 smartphone, without root privileges. The dataset contains over 600 billion data points in over 10 billion data records. Some examples of the sampled sensors are:
Resource utilization per running App (CPU, memory, ...)
Call/SMS logs
Location
WiFi Signal strength
Network statistics
And many more... (see the dataset description here)
These sensors where sampled as a rate rivaling other similar datasets, some features sampled at a rate of up to once every second! More interestingly, we provide explicit labels (timestamps + descriptions) which capture exactly when malware on the device is performing its malicious activities. With these labels, you can use the dataset as a benchmark for your machine learning algorithms.
Additional Details
2.3GB
false
Unknown
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