Skripsi

OPTIMALISASI MULTI-CLASSIFICATION SERANGAN CYBER MENGGUNAKAN METODE K-NEAREST NEIGHBOR. 

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Penilaian

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Cyber attacks are a form of threat aimed at stealing, damaging, or altering important data within computer systems or networks. These attacks include hacking, phishing, and malware. Detecting cyber attacks is crucial to maintaining the security of information systems from increasing threats. One tool used for threat detection is Intrusion Detection Systems (IDS), which monitor system events and take action if there are suspicious activities or attacks. In efforts to enhance IDS performance, research has explored the use of Artificial Intelligence (AI), particularly Machine Learning (ML) techniques. This study focuses on implementing the K-Nearest Neighbors (K-NN) method, a non-parametric technique for measuring the distance between new and previously classified data using Euclidean distance. To test the effectiveness of the K-NN method, various datasets are utilized, including UNSW-NB15, NSL-KDD, ISCX2012, and CIC-IDS-2018. The research findings indicate that the model achieves high accuracy rates on each dataset during the training process, namely 88.00% on the UNSW-NB15 dataset, 99.99% on the NSL-KDD dataset, 99.96% on the ISCX2012 dataset, and 92.00% on the CIC-IDS-2018 dataset.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2407002486	T143138	T1431382024	Central Library (REFERENCES)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T1431382024

Publisher

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

Collation

xxii, 251 hlm.; tab.; ilus.; 29 cm.

Language

Indonesia

ISBN/ISSN

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Classification

005. 109 207

Content Type

Text

Media Type

unmediated

Carrier Type

unspecified

Edition

-

Subject(s)

Prodi Sistem Komputer
Peretas

Specific Detail Info

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Statement of Responsibility

UIN FARRAH

Other version/related

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