Malware Detection using API Calls Visualisations and Convolutional Neural Networks

被引：0

作者：

Pizarro Barona, Jaime ^{[1
]}

Avila Alvarez, Joseph ^{[1
]}

Jimenez Farfan, Carlos ^{[1
]}

Marquez Aguilar, Joangie ^{[1
]}

Bonilla, Rafael I. ^{[1
]}

机构：

[1] Escuela Super Politecn Litoral, Fac Ingn Elect & Computac, Guayaquil, Ecuador

来源：

2023 IEEE/ACM 23RD INTERNATIONAL SYMPOSIUM ON CLUSTER, CLOUD AND INTERNET COMPUTING WORKSHOPS, CCGRIDW | 2023年

关键词：

malware detection; API calls; artificial intelligence; deep learning; convolutional neural networks;

D O I：

10.1109/CCGridW59191.2023.00037

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：153 / 159

页数：7

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