MUGHOLE, KALIMUMBALO DANIELLA and Covenant University, Theses (2023) DEVELOPMENT OF A FEDERATED LEARNING-BASED MALWARE DETECTION MODEL FOR INTERCONNECTED CLOUD INFRASTRUCTURES. Masters thesis, COVENANT UNIVERSITY.
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Abstract
Due to the large number of heterogeneous applications using the same infrastructure, enforcing security and reliability in the cloud is a difficult but crucial task. A security analysis system that detects threats for example malicious software (malware) should exist within the cloud infrastructure. Different malware techniques that bypass network-based and host-based security protections have led to the development of new methods for analysing and detecting malware, which have evolved over the past decades. Due to the complexity of learning the ever-changing configurations of malware, it is challenging for forensics investigators to keep up with the exponential rise in the number and variety of malware species. In this research work, a malware detection model was developed for interconnected cloud infrastructures based on federated learning. This technique enables collaboration between multiple devices in the training of machine learning models without exchanging data, thereby preserving the privacy of individual users. Three different deep-learning algorithms were selected and used in the training, validation, and testing of the models. By the model training with eight clients and twenty-five federation rounds, the FeedForward Neural Networks(FFNN) model provided the best performance with a precision of 84%, an F1-score of 84%, and an accuracy of 84% whereas the Multi-Layer Perceptron(MLP) model provided 83% of precision, 83% of F1-score, and 83% of accuracy and the Long Short-Term Memory(LSTM) model provided a performance with 80% of precision, 80% of F1-score, and 80% of accuracy as well.
| Item Type: | Thesis (Masters) |
|---|---|
| Uncontrolled Keywords: | Federated learning, malware detection, federated cloud, machine learning |
| Subjects: | T Technology > T Technology (General) T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Divisions: | Faculty of Engineering, Science and Mathematics > School of Electronics and Computer Science |
| Depositing User: | AKINWUMI |
| Date Deposited: | 06 Apr 2023 11:58 |
| Last Modified: | 06 Apr 2023 11:58 |
| URI: | http://eprints.covenantuniversity.edu.ng/id/eprint/16775 |
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