A labeled benchmark dataset for training machine learning models to statically detect malicious Windows portable executable files. The dataset includes features extracted from 1.1M binary files: 900K training samples (300K malicious, 300K benign, 300K unlabeled) and 200K test samples (100K malicious, 100K benign).
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MalNet is a large public graph database, representing a large-scale ontology of software function call graphs. MalNet contains over 1.2 million graphs, averaging over 17k nodes and 39k edges per graph, across a hierarchy of 47 types and 696 families.
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We collaborate with Blue Hexagon to release a dataset containing timestamped malware samples and well-curated family information for research purposes. The BODMAS dataset contains 57,293 malware samples and 77,142 benign samples collected from August 2019 to September 2020, with carefully curated family information (581 families). We also provide preprocessed feature vectors and metadata available to everyone. The malware binaries can be obtained per request.
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IoT-23 is a dataset of network traffic from Internet of Things (IoT) devices. It has 20 malware captures executed in IoT devices, and 3 captures for benign IoT devices traffic. It was first published in January 2020, with captures ranging from 2018 to 2019. These IoT network traffic was captured in the Stratosphere Laboratory, AIC group, FEL, CTU University, Czech Republic. Its goal is to offer a large dataset of real and labeled IoT malware infections and IoT benign traffic for researchers to develop machine learning algorithms. This dataset and its research was funded by Avast Software. The malware was allow to connect to the Internet.