DETEKSI SERANGAN BRUTE FORCE MENGGUNAKAN METODE BIDIRECTIONAL RECURRENT NEURAL NETWORKS

RIZON, M. ALFAT HAYATUR and Heryanto, Ahmad and Hermansyah, Adi (2022) DETEKSI SERANGAN BRUTE FORCE MENGGUNAKAN METODE BIDIRECTIONAL RECURRENT NEURAL NETWORKS. Undergraduate thesis, Sriwijaya University.

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Abstract

Brute Force Attack is an attack that targets private information such as usernames, passwords, passphrases and other information. Brute force attack sends combinations continuously in the form of different numbers, letters or symbols, until it gets the right combination so that it can access the protected data. Brute force attack tries to break credential information that is valuable for attackers. The algorithms that are often used in these systems are CNN , Naïve Bayes , SVM , KNN , Decision Tree, Logistic Regression, Random Forest, K-Means, Gradient Bossting, Dimensionality Reduction , Based on the CNN algorithm and brutefoce attack detection, said that the weakness of the cnn method is in the paper, presenting a new framework that integrates local cnn and global cnn both of which are based on the results of the study showing that the CNN-based model is superior to traditional machine learning methods with an accuracy of 94.3%, a precision rate of 92.5%, 97.8% recall rate and 91.8% F1-score in terms of the ability to detect SSH-Brute Froce. forced attack. After a comparative analysis of various classifier models, it was found that the Naive Bayes classifier is very suitable for image classification from its features, many related studies say that the RNN algorithm can overcome the problem that the bi-directional rnn method can solve faster by iteratively so that results emerge the best in the data studied.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Brute force attack, Bi-directional recurrent neural network
Subjects:	T Technology > T Technology (General) > T1-995 Technology (General)
Divisions:	09-Faculty of Computer Science > 56201-Computer Systems (S1)
Depositing User:	M. Alfat Hayatur Rizon
Date Deposited:	13 Jan 2023 01:26
Last Modified:	13 Jan 2023 01:26
URI:	http://repository.unsri.ac.id/id/eprint/85596

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