Trained models and testing datasets used in "Approach for the optimization of machine learning models for calculating binary function similarity"
收藏DataCite Commons2024-07-12 更新2024-07-13 收录
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https://kilthub.cmu.edu/articles/dataset/Trained_models_and_testing_datasets_used_in_Approach_for_the_optimization_of_machine_learning_models_for_calculating_binary_function_similarity_/26042788/1
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资源简介：
This repository contains some trained multi-architecture models and testing datasets for multi-architecture models for the following paper:
Suguru Horimoto, Keane Lucas, and Lujo Bauer. Approach for the optimization of machine learning models for calculating binary function similarity. In <em>Proceedings of the 21st Conference on Detection of Intrusions and Malware & Vulnerability Assessment (DIMVA '24)</em>, 2024.
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In order to use the models and the datasets, please follow the instructions on https://github.com/sgr-ht/mam-for-cbfs
提供机构：
Carnegie Mellon University
创建时间：
2024-07-12
