Malware classification with using deep learning

Year 2022, Volume: 2 Issue: 2, 21 - 40, 31.12.2022

Makbule Damla Yılmaz

Abstract

Today the use of electronic devices, which phones, computers, tablets, etc. has become more and more widespread. As a result of this situation, there is a great increase in the time spent on the internet. Although the widespread use of wireless communication brings about easier access to information, it can sometimes turn the security of data into a threat by malicious people. Malware that threatens information security can cause damage to electronic devices by damaging them, stealing personal information, loss of data of large companies, and causing financial and moral damages to users. For this reason, it has become more important to ensure the security of information while people share many data on the internet uncontrollably. Artificial intelligence methods, which have been developing rapidly in recent years, will undoubtedly become an indispensable part of information security in the near future. In this study, it is aimed to detect and classify malware families by using the Convolutional Neural Networks method, which is in the deep learning subfield of artificial intelligence.

Keywords

Artificial Intelligence, Convolutional Neural Network, Deep Learning, Malicious Software, Malware Detection

References

• Jovanovic, B. A Not So Common Cold: Malware Statistics in 2022. 2022. <https://dataprot.net/statistics/malware-statistics/>
• Cook S. Malware statistics and facts for 2022. 2022. <https://www.comparitech.com/antivirus/malware-statistics-facts/>
• Sihwail, R., Omar, K., Zainol Ariffin, K. A., Al Afghani, S. Malware Detection Approach Based on Artifacts in Memory Image and Dynamic Analysis. 2019; 9(18), 3680. <https://doi.org/10.3390/app9183680>
• Tekerek, A. A novel architecture for web-based attack detection using convolutional neural network. 2021; 100, 102096. <https://doi.org/10.1016/j.cose.2020.102096>
• Bozkir, A. S., Tahillioglu, E., Aydos, M., Kara, I. Catch them alive: A malware detection approach through memory forensics, manifold learning and computer vision. 2021; 103, 102166. <https://doi.org/10.1016/j.cose.2020.102166>
• Tekerek, A., Yapici, M. M. A novel malware classification and augmentation model based on convolutional neural network. 2022; 112, 102515. <https://doi.org/10.1016/j.cose.2021.102515>
• Zhang, Z., Ning, H., Shi, F., Farha, F., Xu, Y., Xu, J., Zhang, F, Choo, K. K. R. Artifcial intelligence in cyber security: research advances, challenges, and opportunities. 2022; 55, 1029-1053. <https://doi.org/10.1007/s10462-021-09976-0>
• Taddeo, M. Three Ethical Challenges of Applications of Artificial Intelligence in Cybersecurity. 2019; 29. <https://doi.org/10.1007/s11023-019-09504-8>
• Gibert, D., Mateu, C., Planes, J. The rise of machine learning for detection and classification of malware: Research developments, trends and challenges. 2020; 153. <https://doi.org/10.1016/j.jnca.2019.102526>
• Alhayani, B., Mohammed, H. J., Chaloob, I. Z., Ahmed, J. S. Effectiveness of artificial intelligence techniques against cyber security risks apply of IT industry. 2021. <https://doi.org/10.1016/j.matpr.2021.02.531>
• Shaukat, K., Luo, S., Varadharajan, V., Hameed, I. A., Chen, S., Liu, D., Li, J. Performance Comparison and Current Challenges of Using Machine Learning Techniques in Cybersecurity. 2020; 13(10)-2509. <https://doi.org/10.3390/en13102509>
• Zhang, Z., Ning, H., Shi, F., Farha, F., Xu, Y., Xu, J., Zhang, F., Choo, K. K. R. Artifcial intelligence in cyber security: research advances, challenges, and opportunities. 2022; 55, 1029-1053. <https://doi.org/10.1007/s10462-021-09976-0>
• Martín, I., Hernández, J. A., de los Santos, S. Machine-Learning based analysis and classification of Android malware signatures. 2019; 97. <https://doi.org/10.1016/j.future.2019.03.006>
• Firdausi, I., Lim, C., Erwin, A. Analysis of machine learning techniques used in behavior-based malware detection. 2010; 2. <https://doi.org/10.1109/ACT.2010.33>
• Li, J., Sun, L., Yan, Q., Li, Z., Srisa-an, W., Ye, H. Significant Permission Identification for Machine Learning Based Android Malware Detection. 2018; 14. <https://doi.org/10.1109/TII.2017.2789219>
• Narudin, F. A., Feizollah, A., Anuar, N. B., Gani, A. Evaluation of machine learning classifiers for mobile malware detection. 2014; 20. <http://dx.doi.org/10.1007/s00500-014-1511-6>
• Jang, S., Li, S., Sung, Y. FastText-Based Local Feature Visualization Algorithm for Merged Image-Based Malware Classification Framework for Cyber Security and Cyber Defense. 2020; 8-460. <http://dx.doi.org/10.3390/math8030460>
• Kang, J., Jang, S., Li, S., Jeong, Y. S., Sung, Y. Long short-term memory-based Malware classification method for information security. 2019; 77. <https://doi.org/10.1016/j.compeleceng.2019.06.014>
• Yan, J., Qi, Y., Rao, Q. Detecting Malware with an Ensemble Method Based on Deep Neural Network. 2018; 2018. <http://dx.doi.org/10.1155/2018/7247095>
• Tekerek, A., Yapici, M. M. A novel malware classification and augmentation model based on convolutional neural network. 2022; 112, 102515. <https://doi.org/10.1016/j.cose.2021.102515>
• Raj, D. D. S., Pal, D. Malware patterns detection and prediction using cloud based deep learning for secured network environment. 2021. <https://doi.org/10.1016/j.matpr.2021.01.611>
• Tekerek, A., Yapici, M. M. A novel malware classification and augmentation model based on convolutional neural network. 2022; 112, 102515. <https://doi.org/10.1016/j.cose.2021.102515>
• Bozkir, A. S., Tahillioglu, E., Aydos, M., Kara, I. Catch them alive: A malware detection approach through memory forensics, manifold learning and computer vision. 2021; 103, 102166. <https://doi.org/10.1016/j.cose.2020.102166>
• Wong, W. K,. Juwono, F. H., Apriono, C. Vision-Based Malware Detection: A Transfer Learning Approach Using Optimal ECOC-SVM Configuration. 2021; 9. <http://dx.doi.org/10.1109/ACCESS.2021.3131713>
• Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., Chen, T. Recent advances in convolutional neural networks. 2018; 77. https://doi.org/10.1016/j.patcog.2017.10.013
• Kumar, B. 2021. <https://medium.com/appyhigh-technology-blog/convolutional-neural-networks-a-brief-history-of-their-evolution-ee3405568597>
• Wang, S. C. Artificial Neural Network, Chapter 5. 2003; 81-100. http://dx.doi.org/10.1007/978-1-4615-0377-4_5
• Goodfellow, I., Bengio, Y., Courville, A. Deep Learning Book. 2018.
• Brownlee, J. 2021. https://machinelearningmastery.com/choose-an-activation-function-for-deep-learning/
• Yamashita, R., Nishio, M., Do, R., Togashi, K. Convolutional neural networks: an overview and application in radiology. 2018. http://dx.doi.org/10.1007/s13244-018-0639-9
• Convolutional Layer. 2018. <https://databricks.com/glossary/convolutional-layer>
• Yamashita, R., Nishio, M., Do, R., Togashi, K. Convolutional neural networks: an overview and application in radiology. 2018. <http://dx.doi.org/10.1007/s13244-018-0639-9>
• https://web.cs.hacettepe.edu.tr/~selman/dumpware10/
• https://web.cs.hacettepe.edu.tr/~selman/dumpware10/
• Tekerek, A., Yapici, M. M. A novel malware classification and augmentation model based on convolutional neural network. 2022; 112, 102515. <https://doi.org/10.1016/j.cose.2021.102515>
• Kızrak, M. A. Derin Öğrenme İçin Aktivasyon Fonksiyonlarının Karşılaştırılması. 2019. https://ayyucekizrak.medium.com/derin-%C3%B6%C4%9Frenme-i%C3%A7in-aktivasyon-fonksiyonlar%C4%B1n%C4%B1n-kar%C5%9F%C4%B1la%C5%9Ft%C4%B1r%C4%B1lmas%C4%B1-cee17fd1d9cd
• Tian, Y., Su, D., Lauria, S., Liue, X. Recent advances on loss functions in deep learning for computer vision. 2022; 497. <https://doi.org/10.1016/j.neucom.2022.04.127>
• Amidi, A., Amidi, S. Derin Öğrenme püf noktaları ve ipuçları el kitabı. <https://stanford.edu/~shervine/l/tr/teaching/cs-230/cheatsheet-deep-learning-tips-and-tricks>
• Amidi, A., Amidi, S. Derin Öğrenme püf noktaları ve ipuçları el kitabı. <https://stanford.edu/~shervine/l/tr/teaching/cs-230/cheatsheet-deep-learning-tips-and-tricks>
• Brownlee, J. Tour of Evaluation Metrics for Imbalanced Classification. 2020. <https://machinelearningmastery.com/tour-of-evaluation-metrics-for-imbalanced-classification/#:~:text=An%20evaluation%20metric%20quantifies%20the,values%20in%20the%20holdout%20dataset.>
• Evaluation Metrics <https://deepai.org/machine-learning-glossary-and-terms/evaluation-metrics>