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PENERAPAN METODE RT-AMD DALAM ANALISIS DETEKSI SERANGAN MALWARE PADA LINGKUNGAN CLOUD COMPUTING

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Penilaian

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The security of cloud computing has become a critical issue with the rise of malware threats such as spyware, ransomware, and trojan horses. This study develops a malware detection system based on RT-AMD (Real-Time Attack Monitoring and Detection) using machine learning algorithms, including Random Forest, Decision Tree, K-Nearest Neighbor, and Naïve Bayes. The CIC-MalMem-2022 dataset is used as the basis for multiclass analysis, categorizing data into benign, spyware, ransomware, and trojan horse. The research stages include preprocessing, applying SMOTE to balance the data, feature selection with Correlation-Based Feature Selection (CFS), and evaluation using accuracy, precision, recall, F1-score, and AUC. The results show that the Random Forest algorithm achieves the highest performance with an accuracy of 83.97%, demonstrating its reliability in effectively detecting malware attack patterns. This method outperforms other algorithms in handling multiclass classification on the CIC-MalMem-2022 dataset. This study provides significant contributions to developing more effective malware detection systems, particularly in cloud computing environments, and opens opportunities for further advancements to address evolving cyber threats.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2507000176	T164399	T1643992024	Central Library (REFERENCE)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T1643992024

Publisher

Indralaya : Prodi Sistem Komputer, Fakultas Ilmu Komputer Universitas Sriwijaya., 2024

Collation

xv, 112 hlm., ilus., tab.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

003.07

Content Type

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Media Type

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Carrier Type

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Edition

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Subject(s)

Sistem komputer

Specific Detail Info

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Statement of Responsibility

EM

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