Article Per Year (5 Year)
p-Index From 2021 - 2026
1.978
P-Index
This Author published in this journals
All Journal Jurnal Informatika Proceeding International Conference on Information Technology and Business International Journal of Artificial Intelligence Research Jurnal Ilmiah Matrik MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer G-Tech : Jurnal Teknologi Terapan SIENNA Pengabdian Kepada Masyarakat Cendekia
Hartono Hartono
Universitas Muhammadiyah Kotabumi
Arts Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Languange, Linguistic, Communication & Media Other

Published : 11 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 11 Documents
Search

 1   2 
Detection of SQL Injection Attacks on MariaDB Using Hybrid Long Short-Term Memory Khotimah, Khusnul; Hartono, Hartono; Apriando, Rama
International Journal of Artificial Intelligence Research Vol 9, No 1.1 (2025)
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v9i1.1.1547

Abstract

This study discusses the development of a SQL Injection attack detection system using the Long Short-Term Memory (LSTM) deep learning model. SQL Injection is a serious security threat to web applications that exploits vulnerabilities in user input to manipulate databases. The LSTM model was chosen due to its ability to process sequential data, which is relevant for analyzing the patterns and structure of SQL queries that are susceptible to attacks. The process begins by collecting and combining datasets from various sources, performing preprocessing to handle duplicate data, missing values, and gibberish queries, as well as analyzing the distribution of query lengths. The textual query data is then converted into a numerical representation through tokenization and padding. The processed dataset is divided into training and testing data. The Bi-directional LSTM model architecture is built with embedding, LSTM, dropout, and dense layers. The model is trained using the training data and its performance is evaluated using the test data, producing metrics such as accuracy, precision, recall, and F1-score. Evaluation results on the test data show a model accuracy of 99.99%, with precision of 99.99%, recall of 99.99%, and F1-score of 99.99% in distinguishing between normal queries and SQL Injection queries. The trained model and the tokenizer used are then saved for further testing purposes. This research demonstrates that the LSTM-based approach is highly effective in detecting SQL Injection attacks with high accuracy. Thus, the model can be deployed at the production level or production server.
Co-Authors Adi Wibowo Aji Wijaya, Ryan Apriando , Rama Apriando, Rama Desi Romadhon Indah Ilya Mustofa Joko Triloka Khusnul Khotimah khusnul khotimah Maharjan, Rokin Muhammad Yudi Abdul Syawari Rahmawati, Anggun Ridha Nurjanah Sigit Gunanto Sriyanto Sriyanto Susilo Hartono Sutedi Sutedi Tia Safitri Triloka, Joko Yudi Abdul Syawari, Muhammad Zikri Zulkarnain