Trained models and testing datasets used in "Approach for the optimization of machine learning models for calculating binary function similarity"

Name: Trained models and testing datasets used in "Approach for the optimization of machine learning models for calculating binary function similarity"
Creator: Carnegie Mellon University
Published: 2024-07-12T17:57:03+08:00

DataCite Commons2024-07-12 更新2024-07-13 收录

机器学习

二进制函数相似性

数据链接：

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 数据链接链接失效反馈

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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. <br> 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