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

NINGRUM, SITI TRIWINARTI and Heryanto, Ahmad (2024) PENERAPAN METODE RT-AMD DALAM ANALISIS DETEKSI SERANGAN MALWARE PADA LINGKUNGAN CLOUD COMPUTING. Undergraduate thesis, Sriwijaya University.

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Abstract

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.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Malware Detection, RT-AMD, Machine learning, Cloud computing
Subjects:	T Technology > T Technology (General) > T1-995 Technology (General)
Divisions:	09-Faculty of Computer Science > 56201-Computer Systems (S1)
Depositing User:	Siti Triwinarti Ningrum
Date Deposited:	14 Jan 2025 01:36
Last Modified:	14 Jan 2025 01:36
URI:	http://repository.unsri.ac.id/id/eprint/164399

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