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All Journal Teknika Jurnal Informatika Proceeding International Conference on Information Technology and Business International Journal of Artificial Intelligence Research Jurnal Teknologi Komputer dan Sistem Informasi Aisyah Journal Of Informatics and Electrical Engineering Jurnal Abdi Insani Vivabio : Jurnal Pengabdian Multidisiplin Jurnal Abdimas Bina Bangsa Jurnal Pengabdian Masyarakat Madani Jurnal Publika Pengabdian Masyarakat
Joko Triloka
Institut Informatika Dan Bisnis Darmajaya
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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 Joko Triloka; Hartono Hartono; Sutedi Sutedi
International Journal of Artificial Intelligence Research Vol 6, No 2 (2022): Desember 2022
Publisher : International Journal of Artificial Intelligence Research

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).
Co-Authors Agung Wibowo Agustinus Eko Setiawan Anandha Sartika Putri Andi Mulyono Aziz, RZ. Abdul Badri, Rico Elhando Cahyani Pratisti Dwi Yana Ayu Andini Hartono Hartono Hary Sabita Jesi Fitriana KURNIA FADILA Kurnia Fadila Kurnia Fadila M. Galih Ramaputra M.Galih Ramaputra Muhammad Purwahid Muhammad Saputra Nisar Zaidal Panji Bintoro Rionaldi Ali Sri Lestari Sutedi Sutedi Tahta Herdian Andika Yuni Puspita Sari Zulkifli Zulkifli Zulkifli