Skripsi

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

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Penilaian

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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

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2307002506	T99829	T1120822023	Central Library (Referens)	Available

Detail Information

Series Title

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Call Number

T1120822023

Publisher

Indralaya : Jurusan Sistem Komputer, Fakultas Ilmu Komputer Universitas Sriwijaya., 2023

Collation

xiv, 71 hlm.; Ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

005.840 7

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)

Jurusan Sistem Komputer
Spyware

Specific Detail Info

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Statement of Responsibility

FIRA

Other version/related

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