DETEKSI POLA SERANGAN ANDROID MALWARE MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK (CNN)

MAULANA, REZA and Stiawan, Deris (2025) DETEKSI POLA SERANGAN ANDROID MALWARE MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK (CNN). Masters thesis, Sriwijaya University.

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Abstract

Android malware is an application that targets Android devices to steal crucial data, including money or confidential information from Android users. Recent years have seen a surge in research on Android malware, as its types continue to evolve, and cybersecurity requires periodic improvements. This research focuses on detecting Android malware attack patterns using deep learning and convolutional neural network (CNN) models, which classify and detect malware attack patterns on Android devices into two categories: malware and non-malware. This research contributes to understanding how effective the CNN models are by comparing the ratio of data used with several epochs. We effectively use CNN models to detect malware attack patterns. The results show that the deep learning method with the CNN model can manage unstructured data. The research results indicate that the CNN model demonstrates a minimal error rate during evaluation. The comparison of accuracy, precision, recall, F1 Score, and area under the curve (AUC) values demonstrates the recognition of malware attack patterns, reaching an average of 92% accuracy in data testing. This provides a holistic understanding of the model's performance and its practical utility in detecting Android malware, for future building of cyber applications.

Item Type:	Thesis (Masters)
Uncontrolled Keywords:	Android Malware, Classification, CNN, Deep Learning, Pattern Recognition.
Subjects:	Q Science > QA Mathematics > QA75-76.95 Calculating machines > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA75-76.95 Calculating machines > QA76 Computer software T Technology > T Technology (General) > T57.6-57.97 Operations research. Systems analysis > T57.6.A2-Z General works Simulation Cf. QA76.9.C65 Computer science Cf. TA343 Engineering mathematics T Technology > T Technology (General) > T57.6-57.97 Operations research. Systems analysis > T57.85 Network systems theory Including network analysis Cf. TS157.5+ Scheduling Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4150-4380 Computer network resources
Divisions:	09-Faculty of Computer Science > 55101-Informatics (S2)
Depositing User:	Reza Maulana
Date Deposited:	04 Jul 2025 08:42
Last Modified:	04 Jul 2025 08:42
URI:	http://repository.unsri.ac.id/id/eprint/176733

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