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All Journal Jurnal Informatika Proceeding International Conference on Information Technology and Business International Journal of Artificial Intelligence Research Jurnal Ilmiah Matrik SIENNA Pengabdian Kepada Masyarakat Cendekia
Hartono Hartono
Universitas Muhammadiyah Kotabumi
Arts Humanities Computer Science & IT Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Other

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Journal : International Journal of Artificial Intelligence Research

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Detection of SQL Injection Attack Using Machine Learning Based On Natural Language Processing Triloka, Joko; Hartono, Hartono; Sutedi, Sutedi
International Journal of Artificial Intelligence Research Vol 6, No 2 (2022): Desember 2022
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (673.401 KB) | DOI: 10.29099/ijair.v6i2.355

Abstract

There has been a significant increase in the number of cyberattacks. This is not only happening in Indonesia, but also in many countries.  Thus, the issue of cyber attacks should receive attention and be interesting to study.  Regarding the explored security vulnerabilities, the Open Web Application Security Project has published the Top-10 website vulnerabilities. SQL Injection is still become one of the website vulnerabiliteis which is often exploited by attacker. This research has implemented and tested five algorithms. They are Naïve Bayes, Logistic Regression, Gradient Boosting, K-Nearest Neighbor, and Support Vector Machine. In addition, this study also uses natural language processing to increase the level of detection accuracy, as a part of text processing. Therefore, the main dataset was converted to corpus to make it easier to be analyzed. This process was carried out on feature enginering stage. This study used two datasets of SQL Injection. The first dataset was used to train the classifier, and the second dataset was used to test the performance of classifier. Based on the tests that have been carried out, the Support Vector Machine get the highest level of accuracy detection. The accuracy of detection is 0.9977 with 0,00100 micro seconds per query time of process. In performance testing, Support Vector Machine classifier can detect 99,37% of second dataset. Not only Support Vector Machine, the study have also revealed the detection accuracy level of further tested algorithms: K-Nearest Neighbor (0,9970), Logistic Refression (0,9960), Gradient Boosting (0,99477), and Naïve Bayes (0,9754).
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 Muhammad Yudi Abdul Syawari Ridha Nurjanah Sigit Gunanto Sriyanto, Sriyanto Susilo Hartono Sutedi Sutedi Tia Safitri Triloka, Joko Yudi Abdul Syawari, Muhammad Zikri Zulkarnain