MALWARE DETECTION USING DEEP LEARNING ALGORITHMS

Year 2023, Volume: 7 Issue: 1, 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

References

• Alzaylaee, M. K., Yerima, S. Y., & Sezer, S. (2020). DL-Droid: Deep learning based android malware detection using real devices. Computers & Security, 89, 101663.
• Aycock, J. (2006). Computer viruses and malware (Vol. 22). Springer Science & Business Media.
• Cheng, Y., Fu, S., Tang, M., & Liu, D. (2019). Multi-task deep neural network (MT-DNN) enabled optical performance monitoring from directly detected PDM-QAM signals. Optics express, 27(13), 19062-19074.
• Danniels, M. "Denial of Service Attacks," Imperva, 2015. [Online]. Available: https://www.incapsula.com/ddos/ddos-attacks/denial-of-service.html. [Accessed 17 Juny 2015]
• He, K., & Kim, D. S. (2019, August). Malware detection with malware images using deep learning techniques. In 2019 18th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/13th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE) (pp. 95-102). IEEE.
• Kedziora, M., Gawin, P., Szczepanik, M., & Jozwiak, I. (2019). Malware detection using machine learning algorithms and reverse engineering of android java code. International Journal of Network Security & Its Applications (IJNSA) Vol, 11.
• Kim, T., Kang, B., Rho, M., Sezer, S., & Im, E. G. (2018). A multimodal deep learning method for android malware detection using various features. IEEE Transactions on Information Forensics and Security, 14(3), 773-788.
• Liu, L., Wang, B. S., Yu, B., & Zhong, Q. X. (2017). Automatic malware classification and new malware detection using machine learning. Frontiers of Information Technology & Electronic Engineering, 18(9), 1336-1347.
• Mao, W., Cai, Z., Towsley, D., Feng, Q., & Guan, X. (2017). Security importance assessment for system objects and malware detection. Computers & Security, 68, 47-68.
• Olah, C., (2015). Understanding LSTM Networks. http://colah.github.io/posts/2015-08-Understanding-LSTMs/, Erişim tarihi 7 Ekim 2020
• Pathak, A., Pakray, P., & Das, R. (2019, February). LSTM neural network based math information retrieval. In 2019 Second International Conference on Advanced Computational and Communication Paradigms (ICACCP) (pp. 1-6). IEEE.
• Qiu, J., Zhang, J., Luo, W., Pan, L., Nepal, S., & Xiang, Y. (2020). A survey of android malware detection with deep neural models. ACM Computing Surveys (CSUR), 53(6), 1-36.
• Singh, J., & Singh, J. (2021). A survey on machine learning-based malware detection in executable files. Journal of Systems Architecture, 112, 101861.
• Talan, T. ve Aktürk, C. (2021) Bilgisayar Biliminde teorik ve uygulamalı araştırmalar, Efe akademi yayıncılık, Istanbul, ISBN: 978-625-8065-42-8
• Tobiyama, S., Yamaguchi, Y., Shimada, H., Ikuse, T., & Yagi, T. (2016, June). Malware detection with deep neural network using process behavior. In 2016 IEEE 40th annual computer software and applications conference (COMPSAC) (Vol. 2, pp. 577-582). IEEE.
• Usman, N., Usman, S., Khan, F., Jan, M. A., Sajid, A., Alazab, M., & Watters, P. (2021). Intelligent dynamic malware detection using machine learning in IP reputation for forensics data analytics. Future Generation Computer Systems, 118, 124-141.
• Vasan, D., Alazab, M., Wassan, S., Naeem, H., Safaei, B., & Zheng, Q. (2020). IMCFN: Image-based malware classification using fine-tuned convolutional neural network architecture. Computer Networks, 171, 107138.
• Vinayakumar, R., Alazab, M., Soman, K. P., Poornachandran, P., & Venkatraman, S. (2019). Robust intelligent malware detection using deep learning. IEEE Access, 7, 46717-46738.
• Wang, Z. J., Turko, R., Shaikh, O., Park, H., Das, N., Hohman, F., ... & Chau, D. H. P. (2020). CNN explainer: learning convolutional neural networks with interactive visualization. IEEE Transactions on Visualization and Computer Graphics, 27(2), 1396-1406.
• Ye, Z., Yang, Y., Li, X., Cao, D., & Ouyang, D. (2018). An integrated transfer learning and multitask learning approach for pharmacokinetic parameter prediction. Molecular pharmaceutics, 16(2), 533-541
• You, W., Shen, C., Wang, D., Chen, L., Jiang, X., & Zhu, Z. (2019). An intelligent deep feature learning method with improved activation functions for machine fault diagnosis. IEEE Access, 8, 1975-1985.
• Soofi, A. A., & Awan, A. (2017). Classification techniques in machine learning: applications and issues. J. Basic Appl. Sci, 13, 459-465.