PENERAPAN METODE HYBRID CNN-LSTM DALAM SISTEM DETEKSI MULTI CLASSIFICATION CYBER ATTACK

FAJRI, MUHAMMAD and Heryanto, Ahmad (2024) PENERAPAN METODE HYBRID CNN-LSTM DALAM SISTEM DETEKSI MULTI CLASSIFICATION CYBER ATTACK. Undergraduate thesis, Sriwijaya University.

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Abstract

In the rapid development of technology, System and Network Security is very important in the ecosystem of digital communication environments. Machine Learning techniques can be used to solve this problem. This research discusses the Machine Learning model to detect Cyber Attack using the Hybrid CNN-LSTM method. In its implementation, CNN is used to select characteristic features from the input data, and send them to LSTM for sequence analysis and overcome the problem of unbalanced data sets. To test the efficiency of the Hybrid CNN-LSTM model implementation, several datasets were used in the implementation of this research, including NSL-KDD, KDDCup1999, ISCX2012, and CIC-IDS-2018. The experimental results show that the model obtained an accuracy of 97.23% on the NSL-KDD dataset, 99.47% on the KDDCup1999 dataset, 99.57% on the ISCX2012 Dataset, and 99.96% on the CIC-IDS-2018 dataset during training.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Hybrid CNN-LSTM, Cyber Attack, NSL-KDD, KDDCup1999, ISCX2012, CIC-IDS-2018
Subjects:	T Technology > T Technology (General) > T1-995 Technology (General)
Divisions:	09-Faculty of Computer Science > 56201-Computer Systems (S1)
Depositing User:	Muhammad Fajri
Date Deposited:	05 Apr 2024 01:53
Last Modified:	05 Apr 2024 01:53
URI:	http://repository.unsri.ac.id/id/eprint/143130

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