Articles
| Open Access |
DOI:
https://doi.org/10.55640/ijcsis/Volume10Issue03-02
Real-Time Malware Detection in Cloud Infrastructures Using Convolutional Neural Networks: A Deep Learning Framework for Enhanced Cybersecurity
Abdullah Al Mamun , Department of Computer & Info Science, Gannon University, Erie, Pennsylvania, USA Ayan Nath , Master’s in computer and information science, International American University Sonjoy Kumar Dey , McComish Department of Electrical Engineering and Computer Science, South Dakota State University, USA Paresh Chandra Nath , Master of Science in Information Technology, Washington University of Science and Technology, USA Md Mohibur Rahman , Fred DeMatteis School of Engineering and Applied Science, Hofstra University, USA Jannatul Ferdous Shorna , College of Engineering and Computer Science, Florida Atlantic University, Boca Raton, Florida Nafis Anjum , College of Technology and Engineering, Westcliff University, Irvine, CAAbstract
This study presents a novel malware detection framework for cloud infrastructures that harnesses the power of Convolutional Neural Networks (CNNs) to achieve real-time threat identification with superior accuracy and speed. Our approach begins with the collection and meticulous preprocessing of heterogeneous cloud log data, followed by advanced feature engineering to extract meaningful patterns indicative of malicious activity. The CNN model automatically learns hierarchical representations from this high-dimensional data, resulting in a detection system that achieves an accuracy of 98.2%, a precision of 97.5%, a recall of 98.0%, and an F1-score of 97.8%. In addition, the model operates with a low latency of 12 ms, a critical factor for timely threat mitigation in dynamic cloud environments. Comparative analysis against Long Short-Term Memory (LSTM), Support Vector Machine (SVM), and Random Forest classifiers reveals that the CNN not only outperforms these models in key performance metrics but also maintains a significant advantage in processing speed. These findings highlight the potential of CNN-based approaches to enhance cybersecurity defenses, offering a scalable and efficient solution for detecting evolving malware threats in cloud infrastructures.
Keywords
Malware Detection, Cloud Infrastructures, Convolutional Neural Networks, Real-time Processing, Feature Engineering
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Copyright (c) 2025 Abdullah Al Mamun, Ayan Nath, Sonjoy Kumar Dey, Paresh Chandra Nath, Md Mohibur Rahman, Jannatul Ferdous Shorna, Nafis Anjum
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