A Novel SQL Injection Detection Using Bi-LSTM and TF-IDF

Imam Ghozali; Misbachul Falach Asy'ari; Sulaiman Triarjo; Hanun Mashita Ramadhani; Hudan Studiawan; Ary Mazharuddin Shiddiqi

doi:10.1109/ICINT55083.2022.00010

A Novel SQL Injection Detection Using Bi-LSTM and TF-IDF

Imam Ghozali, Misbachul Falach Asy'ari, Sulaiman Triarjo, Hanun Mashita Ramadhani, Hudan Studiawan, Ary Mazharuddin Shiddiqi

Institut Teknologi Sepuluh Nopember

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Citations (Scopus)

Abstract

SQL injection is one of the biggest threats to websites. A survey shows more than 300,000 attacks, 24,6% are SQL injection. Detection SQL injection is a complicated task because attackers can continue to change the query structure. Traditionally, SQL injection is detected using a deny list of several keywords, which is commonly used for SQL injection. However, this method is not effective anymore because the attacks have increased significantly. This research develops a technique to detect SQL injection using TF-IDF and Bi-LSTM to produce high accuracy. Experiment results indicate that the proposed method improves SQL injection detection with accuracy, precision, recall, and F1-score reaching 0.99.

Original language	English
Title of host publication	Proceedings - 2022 7th International Conference on Information and Network Technologies, ICINT 2022
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	16-22
Number of pages	7
ISBN (Electronic)	9781665482844
DOIs	https://doi.org/10.1109/ICINT55083.2022.00010
Publication status	Published - 2022
Event	7th International Conference on Information and Network Technologies, ICINT 2022 - Okinawa, Japan Duration: 21 May 2022 → 23 May 2022

Publication series

Name	Proceedings - 2022 7th International Conference on Information and Network Technologies, ICINT 2022

Conference

Conference	7th International Conference on Information and Network Technologies, ICINT 2022
Country/Territory	Japan
City	Okinawa
Period	21/05/22 → 23/05/22

Keywords

Bi-LSTM
SQL Injection
TF-IDF

Access to Document

10.1109/ICINT55083.2022.00010

Cite this

Ghozali, I., Asy'ari, M. F., Triarjo, S., Mashita Ramadhani, H., Studiawan, H., & Shiddiqi, A. M. (2022). A Novel SQL Injection Detection Using Bi-LSTM and TF-IDF. In Proceedings - 2022 7th International Conference on Information and Network Technologies, ICINT 2022 (pp. 16-22). (Proceedings - 2022 7th International Conference on Information and Network Technologies, ICINT 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICINT55083.2022.00010

Ghozali, Imam ; Asy'ari, Misbachul Falach ; Triarjo, Sulaiman et al. / A Novel SQL Injection Detection Using Bi-LSTM and TF-IDF. Proceedings - 2022 7th International Conference on Information and Network Technologies, ICINT 2022. Institute of Electrical and Electronics Engineers Inc., 2022. pp. 16-22 (Proceedings - 2022 7th International Conference on Information and Network Technologies, ICINT 2022).

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title = "A Novel SQL Injection Detection Using Bi-LSTM and TF-IDF",

abstract = "SQL injection is one of the biggest threats to websites. A survey shows more than 300,000 attacks, 24,6% are SQL injection. Detection SQL injection is a complicated task because attackers can continue to change the query structure. Traditionally, SQL injection is detected using a deny list of several keywords, which is commonly used for SQL injection. However, this method is not effective anymore because the attacks have increased significantly. This research develops a technique to detect SQL injection using TF-IDF and Bi-LSTM to produce high accuracy. Experiment results indicate that the proposed method improves SQL injection detection with accuracy, precision, recall, and F1-score reaching 0.99.",

keywords = "Bi-LSTM, SQL Injection, TF-IDF",

author = "Imam Ghozali and Asy'ari, {Misbachul Falach} and Sulaiman Triarjo and {Mashita Ramadhani}, Hanun and Hudan Studiawan and Shiddiqi, {Ary Mazharuddin}",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE. All rights reserved.; 7th International Conference on Information and Network Technologies, ICINT 2022 ; Conference date: 21-05-2022 Through 23-05-2022",

year = "2022",

doi = "10.1109/ICINT55083.2022.00010",

language = "English",

series = "Proceedings - 2022 7th International Conference on Information and Network Technologies, ICINT 2022",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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booktitle = "Proceedings - 2022 7th International Conference on Information and Network Technologies, ICINT 2022",

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Ghozali, I, Asy'ari, MF, Triarjo, S, Mashita Ramadhani, H, Studiawan, H & Shiddiqi, AM 2022, A Novel SQL Injection Detection Using Bi-LSTM and TF-IDF. in Proceedings - 2022 7th International Conference on Information and Network Technologies, ICINT 2022. Proceedings - 2022 7th International Conference on Information and Network Technologies, ICINT 2022, Institute of Electrical and Electronics Engineers Inc., pp. 16-22, 7th International Conference on Information and Network Technologies, ICINT 2022, Okinawa, Japan, 21/05/22. https://doi.org/10.1109/ICINT55083.2022.00010

A Novel SQL Injection Detection Using Bi-LSTM and TF-IDF. / Ghozali, Imam; Asy'ari, Misbachul Falach; Triarjo, Sulaiman et al.
Proceedings - 2022 7th International Conference on Information and Network Technologies, ICINT 2022. Institute of Electrical and Electronics Engineers Inc., 2022. p. 16-22 (Proceedings - 2022 7th International Conference on Information and Network Technologies, ICINT 2022).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - A Novel SQL Injection Detection Using Bi-LSTM and TF-IDF

AU - Ghozali, Imam

AU - Asy'ari, Misbachul Falach

AU - Triarjo, Sulaiman

AU - Mashita Ramadhani, Hanun

AU - Studiawan, Hudan

AU - Shiddiqi, Ary Mazharuddin

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N2 - SQL injection is one of the biggest threats to websites. A survey shows more than 300,000 attacks, 24,6% are SQL injection. Detection SQL injection is a complicated task because attackers can continue to change the query structure. Traditionally, SQL injection is detected using a deny list of several keywords, which is commonly used for SQL injection. However, this method is not effective anymore because the attacks have increased significantly. This research develops a technique to detect SQL injection using TF-IDF and Bi-LSTM to produce high accuracy. Experiment results indicate that the proposed method improves SQL injection detection with accuracy, precision, recall, and F1-score reaching 0.99.

AB - SQL injection is one of the biggest threats to websites. A survey shows more than 300,000 attacks, 24,6% are SQL injection. Detection SQL injection is a complicated task because attackers can continue to change the query structure. Traditionally, SQL injection is detected using a deny list of several keywords, which is commonly used for SQL injection. However, this method is not effective anymore because the attacks have increased significantly. This research develops a technique to detect SQL injection using TF-IDF and Bi-LSTM to produce high accuracy. Experiment results indicate that the proposed method improves SQL injection detection with accuracy, precision, recall, and F1-score reaching 0.99.

KW - Bi-LSTM

KW - SQL Injection

KW - TF-IDF

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DO - 10.1109/ICINT55083.2022.00010

M3 - Conference contribution

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T3 - Proceedings - 2022 7th International Conference on Information and Network Technologies, ICINT 2022

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BT - Proceedings - 2022 7th International Conference on Information and Network Technologies, ICINT 2022

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Ghozali I, Asy'ari MF, Triarjo S, Mashita Ramadhani H, Studiawan H , Shiddiqi AM. A Novel SQL Injection Detection Using Bi-LSTM and TF-IDF. In Proceedings - 2022 7th International Conference on Information and Network Technologies, ICINT 2022. Institute of Electrical and Electronics Engineers Inc. 2022. p. 16-22. (Proceedings - 2022 7th International Conference on Information and Network Technologies, ICINT 2022). doi: 10.1109/ICINT55083.2022.00010

A Novel SQL Injection Detection Using Bi-LSTM and TF-IDF

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