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

HANAYUDA, DWI LINGGA and Heryanto, Ahmad (2023) KLASIFIKASI SERANGAN DDOS PADA APACHE SPARK MENGGUNAKAN METODE LONG SHORT TERM MEMORY. Undergraduate thesis, Sriwijaya University.

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Abstract

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.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	LSTM, DDoS
Subjects:	Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation.
Divisions:	09-Faculty of Computer Science > 56201-Computer Systems (S1)
Depositing User:	Dwi Lingga Hanayuda
Date Deposited:	24 Aug 2023 06:26
Last Modified:	24 Aug 2023 06:26
URI:	http://repository.unsri.ac.id/id/eprint/127817

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