Skripsi

DETEKSI SERANGAN MALWARE BOTNET MENGGUNAKAN METODE SUPPORT VECTOR MACHINE

---

Penilaian

---

A botnet is a collection of devices infected with malware and controlled externally by an attacker to carry out network attacks, such as DDoS attacks, data theft, or spreading spam. This research uses a dataset from CICIoT2023 which consists of three types of classes, namely, tame traffic, mirai greip flood, and mirai udpplain to detect botnet malware attacks using the Support Vector Machine method. The Support Vector Machine method uses three types of kernels, namely, Linear, Polynomial and RBF kernels. The results of this research prove that the Support Vector Machine method using the RBF kernel is capable of detecting botnet malware attacks by achieving the best performance with an accuracy rate of 98.25%, precision of 98.58%, recall of 96.52%, and f1-score of 97.54%.

---

Availability

Inventory Code	Barcode	Call Number	Location	Status
2407004145	T150164	T1501642024	Central Library (Reference)	Available but not for loan - Not for Loan

Detail Information

Series Title

-

Call Number

T1501642024

Publisher

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

Collation

xiii, 50 hlm.; ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

-

Classification

004.07

Content Type

Text

Media Type

unmediated

Carrier Type

-

Edition

-

Subject(s)

Prodi Sistem Komputer
Botnet Detection

Specific Detail Info

-

Statement of Responsibility

KA

Other version/related

File Attachment

Comments

---

You must be logged in to post a comment