MALWARE DETECTION USING DEEP LEARNING ALGORITHMS

Authors : Mohammed ALTAİY, İncilay YILDIZ, Bahadır UÇAN

Pages : 11-26

View : 61 | Download : 58

Publication Date : 2023-06-30

Article Type : Research Paper

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 insert ignore into journalissuearticles values(LSTM);, Convolutional Neural Network insert ignore into journalissuearticles values(CNN);, and Multitasking Deep Neural Network insert ignore into journalissuearticles values(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