Skripsi

PENGEMBANGAN MODEL REINFORCEMENT LEARNING DENGAN ALGORITMA DEEP Q NETWORK (DQN) DAN PROXIMAL POLICY OPTIMIZATION (PPO) UNTUK MULTIKLASIFIKASI SERANGAN SIBER

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Penilaian

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In digital era right now, with the rapid advancement of time, system and network security is very important in the digital communication environment. Machine learning techniques are currently one of the many methods used to address this, including a method known as Reinforcement Learning (RL), which can be used to solve classification problems. In this study, we discuss the application of the Deep Q Network (DQN) and Proximal Policy Optimization (PPO) algorithms from the Reinforcement Learning model to detect multiclass classification of cyber attacks. The impleme’tation was conducted using the ISCX 2012, CICDDoS 2019, KDDCup 1999, and NSL-KDD datasets, and RL successfully learned attack patterns effectively. To evaluate the effectiveness of the model, performance measurements were carried out using the DQN algorithm and achieved an accuracy of 89.27% on the ISCX 2012 dataset, 97.49% on the CICDDoS 2019 dataset, 94.48% on the KDDCup 1999 dataset, and 86.83% on the NSL-KDD dataset. Meanwhile, the PPO algorithm achieved 84.00% on the ISCX 2012 dataset, 87.00% on the CICDDoS 2019 dataset, 95.00% on the KDDCup 1999 dataset, and 85.19% on the NSL-KDD dataset.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2507003378	T175393	T1753932025	Central Library (Reference)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T1753932025

Publisher

Indralaya : Prodi Sistem Komputer, Fakultas Ilmu Komputer Universitas Sriwijaya., 2025

Collation

xvii, 190 hlm., ilus., tab.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

004.07

Content Type

Text

Media Type

-

Carrier Type

-

Edition

-

Subject(s)

Sistem komputer

Specific Detail Info

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Statement of Responsibility

MI

Other version/related

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