An inception V3 approach for malware classification using machine learning and transfer learning

被引：3

作者：

Ahmed M. ^{[1
]}

Afreen N. ^{[1
]}

Ahmed M. ^{[1
]}

Sameer M. ^{[2
]}

Ahamed J. ^{[3
]}

机构：

[1] Jamia Millia Islamia, New Delhi

[2] National Institute of Technology Patna, Bihar

[3] Maulana Azad National Urdu University, Hyderabad

[4] Indian Institute of Technology, Delhi

来源：

International Journal of Intelligent Networks | 2023年 / 4卷

关键词：

Artificial neural network; Convolutional neural network; InceptionV3; Logistic regression; Long short term memory; Microsoft BIG15;

D O I：

10.1016/j.ijin.2022.11.005

中图分类号：

学科分类号：

摘要：

Malware instances have been extremely used for illegitimate purposes, and new variants of malware are observed every day. Machine learning in network security is one of the prime areas of research today because of its performance and has shown tremendous growth in the last decade. In this paper, we formulate the malware signature as a 2D image representation and leverage deep learning approaches to characterize the signature of malware contained in BIG15 dataset across nine classes. The current research compares the performance of various machine learning and deep learning technologies towards malware classification such as Logistic Regression (LR), Artificial Neural Network (ANN), Convolutional Neural Network (CNN), transfer learning on CNN and Long Short Term Memory (LSTM). The transfer learning approach using InceptionV3 resulted in a good performance with respect to the compared models like LSTM with a classification accuracy of 98.76% on the test dataset and 99.6% on the train dataset. © 2022 The Authors

引用

页码：11 / 18

页数：7

共 37 条

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