computer security
The models and results for the paper "GDL-CPA: Cross-Device Profiling Side-channel Analysis with Global Domain-Adversarial Learning".
This dataset include the trained models for cross-device profiling side-channel analysis. Using these models, we can reproduct the results.
The results also give up the detailed obtained from models.
The pictures of paper can be found here.
The related dataset is obtained by : CDPA-SCA/Cross-Device-Profiled-Attack (github.com)
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This dataset is part of my Master's research on malware detection and classification using the XGBoost library on Nvidia GPU. The dataset is a collection of 1.55 million of 1000 API import features extract from jsonl format of the EMBER dataset 2017 v2 and 2018. All data is pre-processing, duplicated records are removed. The dataset contains 800,000 malware and 750,000 "goodware" samples.
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This dataset is part of my PhD research on malware detection and classification using Deep Learning. It contains static analysis data: Top-1000 imported functions extracted from the 'pe_imports' elements of Cuckoo Sandbox reports. PE malware examples were downloaded from virusshare.com. PE goodware examples were downloaded from portableapps.com and from Windows 7 x86 directories.
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This dataset is part of my PhD research on malware detection and classification using Deep Learning. It contains static analysis data: Raw PE byte stream rescaled to a 32 x 32 greyscale image using the Nearest Neighbor Interpolation algorithm and then flattened to a 1024 bytes vector. PE malware examples were downloaded from virusshare.com. PE goodware examples were downloaded from portableapps.com and from Windows 7 x86 directories.
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This dataset is part of my PhD research on malware detection and classification using Deep Learning. It contains static analysis data (PE Section Headers of the .text, .code and CODE sections) extracted from the 'pe_sections' elements of Cuckoo Sandbox reports. PE malware examples were downloaded from virusshare.com. PE goodware examples were downloaded from portableapps.com and from Windows 7 x86 directories.
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This dataset is part of our research on malware detection and classification using Deep Learning. It contains 42,797 malware API call sequences and 1,079 goodware API call sequences. Each API call sequence is composed of the first 100 non-repeated consecutive API calls associated with the parent process, extracted from the 'calls' elements of Cuckoo Sandbox reports.
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