VISUALISASI SERANGAN MALWARE SPYWARE DENGAN MENGGUNAKAN METODE SUPPORT VECTOR MACHINE (SVM)

FITRIANI, ZARAH and Stiawan, Deris (2023) VISUALISASI SERANGAN MALWARE SPYWARE DENGAN MENGGUNAKAN METODE SUPPORT VECTOR MACHINE (SVM). Undergraduate thesis, Sriwijaya University.

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Abstract

Spyware malware attacks are a significant threat to computer hardware and user privacy. To analyze and make it easier to recognize such attacks effectively, visualization techniques can provide valuable insights into their patterns and characteristics. This research using a machine learning algorithm, Support Vector Machine (SVM). The dataset used in this study is from the CIC MalMem 2022 which is benign data and spyware Transponder. Feature selection was applied to this study to identify the features that were most relevant in distinguishing between normal data and attacks. The results obtained using the SVM model with 3 SVM kernels are Linear, Polynomial, and Radial Basis Function (RBF). Based on the validation of the classification process using the SVM kernel got quite high results, where the Linear kernel got the most superior accuracy results, for 8 features accuracy that is 99.53%, 15 features accuracy is 99.77% and 24 features accuracy is 99.96%. Keyword: Spyware, Visualization, Support Vector Machine (SVM), Confusion Matrix

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Spyware, Visualisasi, Support Vector Machine (SVM), Confusion Matrix
Subjects:	T Technology > T Technology (General) > T1-995 Technology (General)
Divisions:	09-Faculty of Computer Science > 56201-Computer Systems (S1)
Depositing User:	Zarah Fitriani
Date Deposited:	19 Jun 2023 08:08
Last Modified:	19 Jun 2023 08:08
URI:	http://repository.unsri.ac.id/id/eprint/112082

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