使用API调用可视化和卷积神经网络的恶意软件检测

2023 IEEE/ACM 23rd International Symposium on Cluster, Cloud and Internet Computing Workshops (CCGridW) Pub Date : 2023-05-01 DOI:10.1109/CCGridW59191.2023.00037

Jaime Pizarro Barona, Joseph Avila Alvarez, Carlos Jiménez Farfán, Joangie Márquez Aguilar, Rafael I. Bonilla

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引用次数: 0

摘要

本研究探索和分析了不同的API调用序列转换方法到图像中，以训练深度学习模型，并确定这些方法和模型的哪种组合效果更好。我们使用Simhash和FreqSeq从API调用序列生成图像。通过训练两种著名的卷积网络架构(ResNet50v2和MobileNetv2)对结果进行比较。这项工作介绍了我们运行这些实验的经验，突出了所获得的结果和我们面临的挑战。

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Malware Detection using API Calls Visualisations and Convolutional Neural Networks

This research explores and analyzes different API Calls sequence transformation methods into images to train deep learning models and determine which combination of these methods and models performs better. We generated images from API Calls sequences using Simhash and FreqSeq. The results were compared by training two well-known Convolutional Network architectures (ResNet50v2 and MobileNetv2). This work presents our experience running these experiments highlighting the results obtained and the challenges we faced.

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来源期刊

2023 IEEE/ACM 23rd International Symposium on Cluster, Cloud and Internet Computing Workshops (CCGridW)

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