Skripsi

PENERAPAN METODE HYBRID CNN-LSTM DALAM SISTEM DETEKSI MULTI CLASSIFICATION CYBER ATTACK

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Penilaian

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In the rapid development of technology, System and Network Security is very important in the ecosystem of digital communication environments. Machine Learning techniques can be used to solve this problem. This research discusses the Machine Learning model to detect Cyber Attack using the Hybrid CNN-LSTM method. In its implementation, CNN is used to select characteristic features from the input data, and send them to LSTM for sequence analysis and overcome the problem of unbalanced data sets. To test the efficiency of the Hybrid CNN-LSTM model implementation, several datasets were used in the implementation of this research, including NSL-KDD, KDDCup1999, ISCX2012, and CIC-IDS-2018. The experimental results show that the model obtained an accuracy of 97.23% on the NSL-KDD dataset, 99.47% on the KDDCup1999 dataset, 99.57% on the ISCX2012 Dataset, and 99.96% on the CIC-IDS-2018 dataset during training.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2407002487	T143130	T1431302024	Central Library (REFERENCES)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T1431302024

Publisher

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

Collation

xvi, 164 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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