KLASIFIKASI SERANGAN SQL INJECTION PADA INTRUSION DETECTION SYSTEM MENGGUNAKAN METODE LONG SHORT TERM MEMORY STACKED

RAHMADANI, TRI PUTRI and Heryanto, Ahmad (2022) KLASIFIKASI SERANGAN SQL INJECTION PADA INTRUSION DETECTION SYSTEM MENGGUNAKAN METODE LONG SHORT TERM MEMORY STACKED. Undergraduate thesis, Sriwijaya University.

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Abstract

SQL Injection is an attack technique by entering malicious SQL commands (queries) and can manipulate command logic to gain access to databases and other sensitive information. The main aim of this attack is to exploit the victim's database to reveal personal information about web applications such as passwords, usernames, secret keys and so on. There are two objectives in this study, among others, to build a Stacked LSTM method model for the ability to classify Sql Injection attacks on the CIC-IDS 2018 dataset. Second, produce a model with performance that is as expected. Therefore, to overcome the previous problem, the deep learning method was used. The Deep Learning method used is the Stacked LSTM method which is a branch of LSTM. In this study the Principal Component Analysis (PCA) technique was used to reduce dimensions and training time efficiency, the Synthetic Minority Over-sampling Technique (SMOTE) technique was also applied to balance the dataset to be processed, then Hyperparameter Tuning is applied to see the best parameters that will be applied to the research model. Research validation was carried out 5 times in the study. The best validation results from the overall results were 90% training data and 10% testing data where in this study the results obtained were 98.76% accuracy, 99.94% recall, 97.59% specificity, 97.64% precision, and F1 -Score 98.78%.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Pengetahuan Guru, Pemodelan Matematika
Subjects:	Q Science > QA Mathematics > QA8.9-QA10.3 Computer science. Artificial intelligence. Computational complexity. Data structures (Computer scienc. Mathematical Logic and Formal Languages
Divisions:	09-Faculty of Computer Science > 56201-Computer Systems (S1)
Depositing User:	Tri Putri Rahmadani
Date Deposited:	06 Jan 2023 08:00
Last Modified:	06 Jan 2023 08:00
URI:	http://repository.unsri.ac.id/id/eprint/85437

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