SISTEM DETEKSI MULTI-CLASSIFICATION SERANGAN CYBER MENGGUNAKAN METODE DEEP LEARNING CNN

NURHALIZA, SARI and Heryanto, Ahmad (2024) SISTEM DETEKSI MULTI-CLASSIFICATION SERANGAN CYBER MENGGUNAKAN METODE DEEP LEARNING CNN. Undergraduate thesis, Sriwijaya University.

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Abstract

Cyber attacks have become an increasing threat to the security of modern information systems. To overcome the complexity and diversity of attacks that occur, a sophisticated and reliable detection approach is needed. In this research, we propose a multi-classification cyber attack detection system that adopts the Deep Learning Convolutional Neural Network (CNN) method. CNNs have proven effective in solving image classification problems and have shown great potential in cyber attack detection applications. We collect a representative dataset of various types of cyber attacks and train a CNN model to identify attacks in multi-classification categories. Experiments were carried out to evaluate the performance of the proposed detection system, including testing the detection speed and accuracy level. The results show that the proposed system is capable of detecting cyber attacks with a high degree of accuracy, while also maintaining sufficient detection speed. The main contribution of this research is the development of a detection system that can provide effective protection against various cyber attacks, by exploiting the power of the Deep Learning CNN method.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Serangan cyber, Deteksi serangan, Deep Learning, Convolutional Neural Network, Multiclassification
Subjects:	T Technology > T Technology (General) > T1-995 Technology (General) T Technology > TA Engineering (General). Civil engineering (General) > TA1-2040 Engineering (General). Civil engineering (General) > TA145 General works. Civil engineering, etc. 1850- T Technology > TA Engineering (General). Civil engineering (General) > TA165 Engineering instruments, meters, etc. Industrial instrumentation
Divisions:	09-Faculty of Computer Science > 56201-Computer Systems (S1)
Depositing User:	Sari Nurhaliza
Date Deposited:	05 Apr 2024 01:52
Last Modified:	05 Apr 2024 01:52
URI:	http://repository.unsri.ac.id/id/eprint/143129

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