Data provided by National Institute of Standards and Technology
TrojAI cyber-network-c2-mar2024 Train DatasetThis is the training data used to create and evaluate trojan detection software solutions. This data, generated at NIST, consists of ResNet18 and ResNet34 neural network models that classify botnet command and control (c2) and benign network traffic packets trained on the USTC-TFC2016 dataset. A known percentage of these trained AI models have been poisoned with a known trigger which induces incorrect behavior. This data will be used to develop software solutions for detecting which trained AI models have been poisoned via embedded triggers.
About this Dataset
Updated: 2024-09-06
Metadata Last Updated:
2024-03-11 00:00:00
Date Created:
N/A
Data Provided by:
Dataset Owner:
N/A
| Title | Trojan Detection Software Challenge - cyber-network-c2-mar2024-train |
|---|---|
| Description | TrojAI cyber-network-c2-mar2024 Train DatasetThis is the training data used to create and evaluate trojan detection software solutions. This data, generated at NIST, consists of ResNet18 and ResNet34 neural network models that classify botnet command and control (c2) and benign network traffic packets trained on the USTC-TFC2016 dataset. A known percentage of these trained AI models have been poisoned with a known trigger which induces incorrect behavior. This data will be used to develop software solutions for detecting which trained AI models have been poisoned via embedded triggers. |
| Modified | 2024-03-11 00:00:00 |
| Publisher Name | National Institute of Standards and Technology |
| Contact | mailto:[email protected] |
| Keywords | Trojan Detection; Artificial Intelligence; AI; Machine Learning; Adversarial Machine Learning; |
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"title": "Trojan Detection Software Challenge - cyber-network-c2-mar2024-train",
"description": "TrojAI cyber-network-c2-mar2024 Train DatasetThis is the training data used to create and evaluate trojan detection software solutions. This data, generated at NIST, consists of ResNet18 and ResNet34 neural network models that classify botnet command and control (c2) and benign network traffic packets trained on the USTC-TFC2016 dataset. A known percentage of these trained AI models have been poisoned with a known trigger which induces incorrect behavior. This data will be used to develop software solutions for detecting which trained AI models have been poisoned via embedded triggers.",
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"theme": [
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