Cloud Computing Malware Detection and Classification Using ANN Based Machine Learning Model
DOI:
https://doi.org/10.18486/ijcsnt/14.3.013Keywords:
ANN, Malware, Malware categorization, Malware identification, Neural networkAbstract
This summary outlines the investigation into detecting and categorizing cloud computing malware using Artificial Neural Network (ANN)-based Machine Learning models. The surge in cloud services has elevated the risk of malware attacks in cloud environments. This research explores the application of ANN-based ML techniques to bolster cloud system security, emphasizing robust malware detection through feature extraction, dataset preparation, and model training. Various ANN architectures are considered and evaluated against real-world cloud malware datasets, revealing the efficacy of ANN models in detecting and classifying cloud-based malware with promising accuracy and efficiency. The deployment of a neural network classifier for binary malware that classifies Windows Portable Executable (PE) files according to imported library function calls is the specific topic of this article. The applied model demonstrates its capacity to generalize against an independent set with an astounding 97.8% average accuracy, 97.6% precision, and 96.6% recall. These findings demonstrate the practicality of the suggested malware categorization technique for bolstering cloud security against changing cyberthreats.
References
Pulsipher, D. W., Scott, A., & Reeb, F. An Argument for a Holistic Approach to Critical Infrastructure Security.
Chaudhary, S., Gkioulos, V., & Katsikas, S. (2023). A quest for research and knowledge gaps in cybersecurity awareness for small and medium-sized enterprises. Computer Science Review, 50, 100592. DOI: https://doi.org/10.1016/j.cosrev.2023.100592
Schmitz-Berndt, S., & Cole, M. D. (2022). Towards an Efficient and Coherent Regulatory Framework on Cybersecurity in the EU: The Proposals for a NIS 2.0 Directive and a Cyber Resilience Act. Applied Cybersecurity & Internet Governance, 1. DOI: https://doi.org/10.5604/01.3001.0016.1323
Tian, D., Zhao, R., Ma, R., Jia, X., Shen, Q., Hu, C., & Liu, W. (2022). MDCD: A malware detection approach in cloud using deep learning. Transactions on Emerging Telecommunications Technologies, 33(11), e4584. DOI: https://doi.org/10.1002/ett.4584
Zheng, L., & Zhang, J. (2022). A new malware detection method based on vmcadr in cloud environments. Security and Communication Networks, 2022. DOI: https://doi.org/10.1155/2022/4208066
Ramesh, G., Logeshwaran, J., & Aravindarajan, V. (2022). The Performance Evolution of Antivirus Security Systems in Ultra dense Cloud Server Using Intelligent Deep Learning. BOHR International Journal of Computational Intelligence and Communication Network, 1(1), 15-19. DOI: https://doi.org/10.54646/bijcicn.004
Mishra, P., Gupta, A., Aggarwal, P., & Pilli, E. S. (2022). vServiceInspector: Introspection-assisted evolutionary bag-of-ngram approach to detect malware in cloud servers. Ad Hoc Networks, 131, 102836. DOI: https://doi.org/10.1016/j.adhoc.2022.102836
Nguyen, P. S., Cuong, N. N., & Long, H. V. (2023). Cloud-Based Malware Detection Using Machine Learning Methods. In Risk Detection and Cyber Security for the Success of Contemporary Computing (pp. 171-197). IGI Global. DOI: https://doi.org/10.4018/978-1-6684-9317-5.ch009
Singh, A., Mushtaq, Z., Abosaq, H. A., Mursal, S. N. F., Irfan, M., & Nowakowski, G. (2023). Enhancing ransomware attack detection using transfer learning and deep learning ensemble models on cloud-encrypted data. Electronics, 12(18), 3899. DOI: https://doi.org/10.3390/electronics12183899
Arunkumar, M., & Kumar, K. A. (2023). GOSVM: Gannet optimization based support vector machine for malicious attack detection in cloud environment. International Journal of Information Technology, 15(3), 1653-1660. DOI: https://doi.org/10.1007/s41870-023-01192-z
Al-Saedi, K. H. (2023). Enhancing cloud security through the integration of deep learning and data mining techniques: A comprehensive review. Periodicals of Engineering and Natural Sciences, 11(3), 176-192. DOI: https://doi.org/10.21533/pen.v11.i3.144
Hesabi, M., & Deypir, M. (2023). An Improved Method for Malware Attack Detection in Cloud Computing Using Collective Learning. Scientific Journal of Electronical & Cyber Defence, 10(4).
Ramish, M., Sinha, A., Desai, J., Raj, A., Rajawat, Y. S., & Punia, P. (2022, April). IT Attack Detection and Classification using Users Event Log Feature And Behavior Analytics through Fourier EEG Signal. In 2022 IEEE 11th International Conference on Communication Systems and Network Technologies (CSNT) (pp. 577-582). IEEE. DOI: https://doi.org/10.1109/CSNT54456.2022.9787637
Miranda, T. J. C. (2022). Profiling and Visualizing Android Malware Datasets (Doctoral disertation, CentraleSupélec)
Aljuhani, A., Kumar, P., Kumar, R., Jolfaei, A., & Islam, A. N. (2022). Fog intelligence for secure smart villages: Architecture, and future challenges. IEEE Consumer Electronics Magazine. DOI: https://doi.org/10.1109/MCE.2022.3193268
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Anurag Sinha, Harsh Raj, Michael Wiryaseputra, Mini kumari, Bhaskar Singh, Ahmed Alkhayyat
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
