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OPTIMALISASI KLASIFIKASI MULTICLASS SERANGAN SIBER DENGAN ALGORITMA PROXIMAL POLICY OPTIMIZATION (PPO) DAN ADVANTAGE ACTOR-CRITIC (A2C) PADA REINFORCEMENT LEARNING

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Penilaian

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The Proximal Policy Optimization (PPO) and Advantage Actor-Critic (A2C) methods have proven effective in detecting, evaluating, and performing multi-class classification of cyberattacks. This study implements both algorithms within a Reinforcement Learning framework to classify types of attacks based on network traffic features. The datasets used for testing include CIC-IDS2018, CIC-IDS2017, ISCX2012, and NSL-KDD. Each dataset undergoes preprocessing, normalization, and feature selection using the SelectKBest method to obtain the most relevant features. Experimental results show that both PPO and A2C algorithms are capable of detecting attacks with high accuracy, with performance variations depending on the characteristics of the dataset. The PPO method excels in training stability and reward utilization, while A2C demonstrates strong adaptability to continuous exploitation strategies. With a careful approach to feature selection, data ratio, and model parameters, this system can deliver accurate and efficient detection in modern multi-class cyberattack classification

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2507003369	T175324	T1753242025	Central Library (Reference)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T1753242025

Publisher

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

Collation

xvi, 153 hlm., ilus., tab.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

004.07

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)

Sistem komputer

Specific Detail Info

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Statement of Responsibility

EM

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