Skripsi

KLASIFIKASI SERANGAN DDOS PADA APACHE SPARK MENGGUNAKAN METODE LONG SHORT TERM MEMORY

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Penilaian

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Distributed Denial-of-Service (DDoS) attacks pose a serious threat to computer network infrastructures. Apache Spark, as a popular distributed data processing platform, requires effective methods to classify DDoS attacks on Apache Spark in order to enhance system security. In this research, we propose a classification approach using the Long-Short Term Memory (LSTM) method, data preprocessing with normalization, and dataset separation into training and testing data. We construct an optimized LSTM model architecture, which is trained using the training data and tested using the testing data to classify DDoS attacks. The CIC-IDS2018 dataset is used in this study. The experimental results demonstrate that the LSTM method achieves good performance in classifying DDoS attacks on Apache Spark. The LSTM model achieves high accuracy and exhibits good evaluation metrics such as precision, recall, and F1-score. Thus, the LSTM method can serve as an effective solution for securing Apache Spark against DDoS attacks.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2307004076	T127817	T1278172023	Central Library (Reference)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T1278172023

Publisher

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

Collation

xiv, 77 hlm.; Ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

005.807

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)

Keamanan Data
Jurusan Sistem Komputer

Specific Detail Info

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Statement of Responsibility

FIRA

Other version/related

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