MALWARE DETECTION USING DEEP LEARNING ALGORITHMS

Year 2023, , 11 - 26, 30.06.2023

Mohammed Altaiy , İncilay Yıldız , Bahadır Uçan

https://doi.org/10.53600/ajesa.1321170

Cited By: 2

Abstract

Background/aim: The aim of this study is to benefit from deep learning algorithms in the classification of malware. It is to determine the most effective classification algorithm by comparing the performances of deep learning algorithms.
Materials and methods: In this study, three deep learning methods, namely Long-Short-Term Memory Network (LSTM), Convolutional Neural Network (CNN), and Multitasking Deep Neural Network (DNN) were used.
Results: According to the findings obtained in malware detection, the best results were obtained from LTSM, CNN and DNN methods, respectively. With the three deep learning algorithms, the average Accuracy was 96%, the Precision average was 97%, and the Recall average was 97%.
Conclusion: According to the most effective results obtained from this study, Accuracy 0.982, Precision 0.988 and Recall 0.990.

Keywords

ber Security, Deep Learning, Classification

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