Skripsi

KLASIFIKASI SERANGAN SPYWARE ANDROID MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK (CNN)

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The increasing use of Android devices has led to a rise in security threats, particularly spyware attacks that threaten user privacy. Conventional signature-based detection methods have limitations in detecting new spyware variants. This study aims to classify Android spyware attacks using the Convolutional Neural Network (CNN) method. The dataset used is CIC-MalMem2022, consisting of memory dump extraction data classified into benign and spyware classes. The research process includes data preprocessing, data balancing, data splitting, and CNN model training. Model performance is evaluated using K-Fold validation and confusion matrix metrics, including accuracy, precision, recall, and F1-score. The results show that CNN achieves high accuracy and stable performance in classifying Android spyware attacks.

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Availability

Detail Information

Series Title

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Call Number

T1922872025

Publisher

Indralaya : Prodi Sistem Komputer, Fakultas Ilmu Komputer Universitas Sriwijaya., 2025

Collation

vx, 82 hlm.; ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

005.807

Content Type

Text

Media Type

unmediated

Carrier Type

-

Edition

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Subject(s)

Prodi Sistem Komputer
Metode CNN

Specific Detail Info

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Statement of Responsibility

KA

Other version/related

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