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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-1263
Malware Training Sets
External Dataset
External Data Source
GitHub
Unknown
Unknown
56 (lowest rank is 56)
Description
Aim of the project is to provide an useful and classified dataset to researchers who want to investigate deeper in malware analysis by using Machine Learning techniques.
One of the most challenging tasks during Machine Learning processing is to define a great training (and possible dynamic) dataset. Assuming a well known learning algorithm and a periodic learning supervised process what you need is a classified dataset to best train your machine. Thousands of training datasets are available out there, but no great classified datasets for malware analyses exist. This dataset was created to share with the scientific community (and everybody interested on it) in order to give to everyone a base point to start with Machine Learning for Malware Analysis.
The collected dataset is composed by the following samples:
APT1 292 Samples
Crypto 2024 Samples
Locker 434 Samples
Zeus 2014 Samples
Additional Details
31.6MB
false
Unknown
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