Skripsi

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

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Penilaian

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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.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2507003795	T176733	T1767332025	Central Library (Reference)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T1767332025

Publisher

Indralaya : Program Magister Ilmu Komputer, Fakultas Ilmu Komputer Universitas Sriwijaya., 2025

Collation

xii, 131 hlm.; ilus.; tab.; 29 cm.

Language

Indonesia

ISBN/ISSN

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Classification

004.07

Content Type

Text

Media Type

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Carrier Type

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Edition

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Subject(s)

Ilmu Komputer

Specific Detail Info

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Statement of Responsibility

MI

Other version/related

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