Detection of SQL Injection Attacks Based on Supervised Machine Learning Algorithms: A Review
Keywords:
SQL Injection, Machine Learning, Supervised ML Algorithms
Abstract
In the ever-changing world of cybersecurity, it is becoming more important to ensure integrity of web applications as well as securing sensitive data. Among a variety of vulnerabilities, SQL injection is considered a significant risk with severe consequences. Addressing this crucial threat has always attracted the researchers to explore various approaches to identify and detect SQL injection attacks. The machine learning has captured the attention of the researchers to explore its potential due to its success in several different fields and the limitation of other rule-based approaches. This study provides a comprehensive review on a variety of the most recent researches that have been carried out using supervised learning algorithms. The study reveals that machine learning has a huge potential in the process of identification and detection of SQL injection attacks.
References
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Abdulmalik, Y. (2021). An Improved SQL Injection Attack Detection Model Using Machine Learning Techniques. International Journal of Innovative Computing, 11(1), pp. 53–57.
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Ashlam, A. A., Badii, A. & Stahl, F. (2022). A Novel Approach Exploiting Machine Learning to Detect Sqli Attacks. In 2022 5th International Conference on Advanced Systems and Emergent Technologies (ICASET), pp. 513–517.
Azman, M. A., Marhusin, M. F. & Sulaiman, R. (2021). Machine Learning-Based Technique to Detect SQL Injection Attack. Journal f Computer Science.
Bharati, V. & Kumar, A. (2022). An Efficient Approach Toward Security of Web Application Using SQL Attack Detection and Prevention Technique. In Inventive Computation and Information Technologies: Proceedings Of ICICIT 2021, pp. 781–792. Springer.
Brindavathi, B., Karrothu, A. & Anilkumar, C. (2023). An Analysis of AI-Based SQL Injection (Sqli) Attack Detection. In 2023 Second International Conference on Augmented Intelligence and Sustainable Systems (ICAISS), pp. 31–35.
Crespo-Mart’Inez, I. S., Campazas-Vega, A., Guerrero-Higueras, Á. M., Riego-Delcastillo, V., Álvarez-Aparicio, C. & Fernández-Llamas, C. (2023). SQL Injection Attack Detection in Network Flow Data. Computers and Security, 127, pp. 103093.
Demilie, W. B. & Deriba, F. G. (2022). Detection and Prevention Of SQLI Attacks and Developing Compressive Framework Using Machine Learning and Hybrid Techniques. Journal of Big Data, 9(1), pp. 124.
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Erd\Hodi, L., Sommervoll, Å. Å. & Zennaro, F. M. (2021). Simulating SQL Injection Vulnerability Exploitation Using Q-Learning Reinforcement Learning Agents. Journal of Information Security and Applications, 61, pp. 102903.
Falor, A., Hirani, M., Vedant, H., Mehta, P. & Krishnan, D. (2022). A Deep Learning Approach for Detection Of SQL Injection Attacks Using Convolutional Neural Networks. In Proceedings of Data Analytics and Management: ICDAM 2021, Volume 2, pp. 293–304.
Fidalgo, A., Medeiros, I., Antunes, P. & Neves, N. (2020). Towards A Deep Learning Model for Vulnerability Detection on Web Application Variants. In 2020 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW), pp. 465–476.
Gandhi, N., Patel, J., Sisodiya, R., Doshi, N. & Mishra, S. (2021). A CNN-Bilstm Based Approach for Detection of SQL Injection Attacks. In 2021 International Conference on Computational Intelligence And Knowledge Economy (ICCIKE), pp. 378–383.
Goyal, A. & Matta, P. (2023). Beyond The Basics: A Study Of Advanced Techniques for Detecting and Preventing SQL Injection Attacks. In 2023 4th International Conference on Smart Electronics And Communication (ICOSEC), pp. 628–631.
Gupta, R. & Others. (2020). A Survey On Machine Learning Approaches and Its Techniques. In 2020 IEEE International Students’ Conference on Electrical, Electronics and Computer Science (SCEECS), pp. 1–6.
Gupta, V., Mishra, V. K., Singhal, P. & Kumar, A. (2022). An Overview of Supervised Machine Learning Algorithm. In 2022 11th International Conference on System Modeling and Advancement In Research Trends (SMART), pp. 87–92.
Hasan, M., Balbahaith, Z. & Tarique, M. (2019). Detection Of SQL Injection Attacks: A Machine Learning Approach. In 2019 International Conference On Electrical And Computing Technologies And Applications (ICECTA), pp. 1–6.
Hassan, M. M., Risha, R. & Esha, A. (2022). ADT-Sqli: An Automated Detection Of SQL Injection Vulnerability In Web Applications. In Proceedings Of International Conference on Frontiers In Computing And Systems: COMSYS 2021, pp. 433–443.
Hubskyi, O., Babenko, T., Myrutenko, L. & Oksiiuk, O. (2020). Detection Of Sql Injection Attack Using Neural Networks. In International Scientific-Practical Conference, pp. 277–286.
Ibrohim, M. M. & Suryani, V. (2023). Classification Of SQL Injection Attacks Using Ensemble Learning SVM And Na"Ive Bayes. In 2023 International Conference on Data Science and Its Applications (Icodsa), pp. 230–236.
Islam, M. R. U., Islam, M. S., Ahmed, Z., Iqbal, A. & Shahriyar, R. (2019). Automatic Detection Of Nosql Injection Using Supervised Learning. In 2019 IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC). Vol. 1, pp. 760–769.
Jemal, I., Cheikhrouhou, O., Hamam, H. & Mahfoudhi, A. (2020). Sql Injection Attack Detection and Prevention Techniques Using Machine Learning. International Journal Of Applied Engineering Research, 15(6), pp. 569–580.
Johny, J. H. B., Nordin, W. A. F. B., Lahapi, N. M. B. & Leau, Y.-B. (2021). SQL Injection Prevention in Web Application: A Review. In Advances In Cyber Security: Third International Conference, Aces 2021, Penang, Malaysia, August 24-25, 2021, Revised Selected Papers 3, pp. 568–585.
Krishnan, S. A., Sabu, A. N., Sajan, P. P. & Sreedeep, A. (2021). SQL Injection Detection Using Machine Learning. Vol, 11, pp. 11.
Kunang, Y. N., Nurmaini, S., Stiawan, D. & Suprapto, B. Y. (2021). Attack Classification of an Intrusion Detection System Using Deep Learning and Hyperparameter Optimization. Journal Of Information Security And Applications, 58, pp. 102804.
Lakhani, S., Yadav, A. & Singh, V. (2022). Detecting SQL Injection Attack Using Natural Language Processing. In 2022 IEEE 9th Uttar Pradesh Section International Conference On Electrical, Electronics And Computer Engineering (UPCON), pp. 1–5.
Li, Q., Li, W., Wang, J. & Cheng, M. (2019). A SQL Injection Detection Method Based on Adaptive Deep Forest. IEEE Access, 7, pp. 145385–145394.
Luo, A., Huang, W. & Fan, W. (2019). A CNN-Based Approach to The Detection of SQL Injection Attacks. In 2019 IEEE/ACIS 18th International Conference on Computer and Information Science (ICIS), pp. 320–324.
Mcivera, A., Rabehajaa, T., Wenb, R. & Morganb, C. (2017). Journal of Information Security and Applications.
Mondal, B., Banerjee, A. & Gupta, S. (2022). A Review of SQLI Detection Strategies Using Machine Learning. Machine Learning, 6(S2), pp. 9664–9677.
Nasereddin, M., Alkhamaiseh, A., Qasaimeh, M. & Al-Qassas, R. (2023). A Systematic Review of Detection And Prevention Techniques of SQL Injection Attacks. Information Security Journal: A Global Perspective, 32(4), pp. 252–265.
Natarajan, Y., Karthikeyan, B., Wadhwa, G., Srinivasan, S. & Akilesh, A. P. (2022). A Deep Learning Based Natural Language Processing Approach for Detecting SQL Injection Attack. In International Conference on Intelligent Systems Design and Applications, pp. 396–406.
Padmaja, B., Sekhar, G. C., Rama Padmaja, C. V., Chandana, P. & Krishna Rao Patro, E. (2022). Tool-Based Prediction of SQL Injection Vulnerabilities and Attacks on Web Applications. In Communication, Software and Networks: Proceedings of India 2022, pp. 535–543. Springer.
Pinzon, C. I., De Paz, J. F., Herrero, A., Corchado, E., Bajo, J. & Corchado, J. M. (2013). Idmas-SQL: Intrusion Detection Based on MAS To Detect and Block SQL Injection Through Data Mining. Information Sciences, 231, pp. 15–31.
Praveen, S., Dcouth, A. & Mahesh, A. (2022). Nosql Injection Detection Using Supervised Text Classification. In 2022 2nd International Conference on Intelligent Technologies (CONIT), pp. 1–5.
Ross, K., Moh, M., Moh, T.-S. & Yao, J. (2018). Multi-Source Data Analysis And Evaluation Of Machine Learning Techniques For SQL Injection Detection. In Proceedings of The ACMSE 2018 Conference, pp. 1–8.
Roy, P., Kumar, R. & Rani, P. (2022). SQL Injection Attack Detection By Machine Learning Classifier. In 2022 International Conference on Applied Artificial Intelligence and Computing (ICAAIC), pp. 394–400.
Sadeeq, H. T. & Abdulazeez, A. M. (2023). Metaheuristics: A Review of Algorithms. International Journal of Online and Biomedical Engineering, 19(9).
Sadeeq, H. T., Ameen, S. Y. & Abdulazeez, A. M. (2022). Cancer Diagnosis Based On Artificial Intelligence, Machine Learning, And Deep Learning. In 2022 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT), pp. 656–661.
Saleem, S., Sheeraz, M., Hanif, M. & Farooq, U. (2020). Web Server Attack Detection Using Machine Learning. In 2020 International Conference on Cyber Warfare and Security (ICCWS), pp. 1–7.
Salih, A. A. & Abdulazeez, A. M. (2021). Evaluation Of Classification Algorithms For Intrusion Detection System: A Review. Journal Of Soft Computing and Data Mining, 2(1), pp. 31–40.
Saran, M., Yadav, R. K., Maurya, P., Devi, S. & Tripathi, U. N. (2022). A Comprehensive Review For Detection And Prevention Techniques For SQL Injection Attack In Cloud Computing. International Journal of Innovative Research In Engineering and Management, 9(5), pp. 11–17.
Singh, G., Kant, D., Gangwar, U. & Singh, A. P. (2015). Sql Injection Detection And Correction Using Machine Learning Techniques. In Emerging ICT For Bridging The Future-Proceedings Of The 49th Annual Convention of The Computer Society Of India (CSI) Volume 1, pp. 435–442.
Sivasangari, A., Jyotsna, J. & Pravalika, K. (2021). SQL Injection Attack Detection Using Machine Learning Algorithm. In 2021 5th International Conference on Trends in Electronics And Informatics (ICOEI), pp. 1166–1169.
Sommervoll, Å. Å., Erd\Hodi, L. & Zennaro, F. M. (2023). Simulating All Archetypes Of SQL Injection Vulnerability Exploitation Using Reinforcement Learning Agents. International Journal of Information Security, pp. 1–22.
Tang, P., Qiu, W., Huang, Z., Lian, H. & Liu, G. (2020). Detection of SQL Injection Based on Artificial Neural Network. Knowledge-Based Systems, 190, pp. 105528.
Tasevski, I. & Jakimoski, K. (2020). Overview Of Sql Injection Defense Mechanisms. In 2020 28th Telecommunications Forum (TELFOR).
Tripathy, D., Gohil, R. & Halabi, T. (2020). Detecting SQL Injection Attacks In Cloud Saas Using Machine Learning. In 2020 IEEE 6th Intl Conference on Big Data Security on Cloud (Bigdatasecurity), IEEE Intl Conference on High Performance and Smart Computing,(HPSC) and IEEE Intl Conference on Intelligent Data and Security (IDS), pp. 145–150.
Xie, X., Ren, C., Fu, Y., Xu, J. & Guo, J. (2019). Sql Injection Detection For Web Applications Based On Elastic-Pooling Cnn. IEEE Access, 7, pp. 151475–151481.
Zhang, K. (2019). A Machine Learning Based Approach To Identify SQL Injection Vulnerabilities. In 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1286–1288.
Zhang, T. & Guo, X. (2020). Research On SQL Injection Vulnerabilities And Its Detection Methods. In 2020 4th Annual International Conference on Data Science and Business Analytics (ICDSBA), pp. 251–254.
Zhumabekova, A., Matson, E. T., Karyukin, V., Zhumabekova, K., Zhuandykov, B., Ussatova, O. & Telbayeva, T. (2023). Determining Web Application Vulnerabilities Using Machine Learning Methods. In 2023 19th International Asian School-Seminar on Optimization Problems of Complex Systems (OPCS), pp. 136–139.
Zolanvari, M., Teixeira, M. A., Gupta, L., Khan, K. M. & Jain, R. (2019). Machine Learning-Based Network Vulnerability Analysis of Industrial Internet of Things. IEEE Internet of Things Journal, 6(4), pp. 6822–6834.
Abdullah, R. M., Abdulazeez, A. M. & Al-Zebari, A. (2021). Machine Learning Algorithm of Intrusion Detection System. Asian Journal of Research in Computer Science, 9(3), pp. 1–12.
Abdulmalik, Y. (2021). An Improved SQL Injection Attack Detection Model Using Machine Learning Techniques. International Journal of Innovative Computing, 11(1), pp. 53–57.
Ahmed, M. & Uddin, M. N. (2020). Cyber Attack Detection Method Based on Nlp and Ensemble Learning Approach. In 2020 23rd International Conference on Computer and Information Technology (ICCIT), pp. 1–6.
Ashlam, A. A., Badii, A. & Stahl, F. (2022). A Novel Approach Exploiting Machine Learning to Detect Sqli Attacks. In 2022 5th International Conference on Advanced Systems and Emergent Technologies (ICASET), pp. 513–517.
Azman, M. A., Marhusin, M. F. & Sulaiman, R. (2021). Machine Learning-Based Technique to Detect SQL Injection Attack. Journal f Computer Science.
Bharati, V. & Kumar, A. (2022). An Efficient Approach Toward Security of Web Application Using SQL Attack Detection and Prevention Technique. In Inventive Computation and Information Technologies: Proceedings Of ICICIT 2021, pp. 781–792. Springer.
Brindavathi, B., Karrothu, A. & Anilkumar, C. (2023). An Analysis of AI-Based SQL Injection (Sqli) Attack Detection. In 2023 Second International Conference on Augmented Intelligence and Sustainable Systems (ICAISS), pp. 31–35.
Crespo-Mart’Inez, I. S., Campazas-Vega, A., Guerrero-Higueras, Á. M., Riego-Delcastillo, V., Álvarez-Aparicio, C. & Fernández-Llamas, C. (2023). SQL Injection Attack Detection in Network Flow Data. Computers and Security, 127, pp. 103093.
Demilie, W. B. & Deriba, F. G. (2022). Detection and Prevention Of SQLI Attacks and Developing Compressive Framework Using Machine Learning and Hybrid Techniques. Journal of Big Data, 9(1), pp. 124.
Deriba, F., Salau, A. O., Mohammed, S. H., Kassa, T. M. & Demilie, W. B. (2022). Development of a Compressive Framework Using Machine Learning Approaches for SQL Injection Attacks. PRZEGLKad ELEKTROTECHNICZNY, 1(7), pp. 183–189.
Erd\Hodi, L., Sommervoll, Å. Å. & Zennaro, F. M. (2021). Simulating SQL Injection Vulnerability Exploitation Using Q-Learning Reinforcement Learning Agents. Journal of Information Security and Applications, 61, pp. 102903.
Falor, A., Hirani, M., Vedant, H., Mehta, P. & Krishnan, D. (2022). A Deep Learning Approach for Detection Of SQL Injection Attacks Using Convolutional Neural Networks. In Proceedings of Data Analytics and Management: ICDAM 2021, Volume 2, pp. 293–304.
Fidalgo, A., Medeiros, I., Antunes, P. & Neves, N. (2020). Towards A Deep Learning Model for Vulnerability Detection on Web Application Variants. In 2020 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW), pp. 465–476.
Gandhi, N., Patel, J., Sisodiya, R., Doshi, N. & Mishra, S. (2021). A CNN-Bilstm Based Approach for Detection of SQL Injection Attacks. In 2021 International Conference on Computational Intelligence And Knowledge Economy (ICCIKE), pp. 378–383.
Goyal, A. & Matta, P. (2023). Beyond The Basics: A Study Of Advanced Techniques for Detecting and Preventing SQL Injection Attacks. In 2023 4th International Conference on Smart Electronics And Communication (ICOSEC), pp. 628–631.
Gupta, R. & Others. (2020). A Survey On Machine Learning Approaches and Its Techniques. In 2020 IEEE International Students’ Conference on Electrical, Electronics and Computer Science (SCEECS), pp. 1–6.
Gupta, V., Mishra, V. K., Singhal, P. & Kumar, A. (2022). An Overview of Supervised Machine Learning Algorithm. In 2022 11th International Conference on System Modeling and Advancement In Research Trends (SMART), pp. 87–92.
Hasan, M., Balbahaith, Z. & Tarique, M. (2019). Detection Of SQL Injection Attacks: A Machine Learning Approach. In 2019 International Conference On Electrical And Computing Technologies And Applications (ICECTA), pp. 1–6.
Hassan, M. M., Risha, R. & Esha, A. (2022). ADT-Sqli: An Automated Detection Of SQL Injection Vulnerability In Web Applications. In Proceedings Of International Conference on Frontiers In Computing And Systems: COMSYS 2021, pp. 433–443.
Hubskyi, O., Babenko, T., Myrutenko, L. & Oksiiuk, O. (2020). Detection Of Sql Injection Attack Using Neural Networks. In International Scientific-Practical Conference, pp. 277–286.
Ibrohim, M. M. & Suryani, V. (2023). Classification Of SQL Injection Attacks Using Ensemble Learning SVM And Na"Ive Bayes. In 2023 International Conference on Data Science and Its Applications (Icodsa), pp. 230–236.
Islam, M. R. U., Islam, M. S., Ahmed, Z., Iqbal, A. & Shahriyar, R. (2019). Automatic Detection Of Nosql Injection Using Supervised Learning. In 2019 IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC). Vol. 1, pp. 760–769.
Jemal, I., Cheikhrouhou, O., Hamam, H. & Mahfoudhi, A. (2020). Sql Injection Attack Detection and Prevention Techniques Using Machine Learning. International Journal Of Applied Engineering Research, 15(6), pp. 569–580.
Johny, J. H. B., Nordin, W. A. F. B., Lahapi, N. M. B. & Leau, Y.-B. (2021). SQL Injection Prevention in Web Application: A Review. In Advances In Cyber Security: Third International Conference, Aces 2021, Penang, Malaysia, August 24-25, 2021, Revised Selected Papers 3, pp. 568–585.
Krishnan, S. A., Sabu, A. N., Sajan, P. P. & Sreedeep, A. (2021). SQL Injection Detection Using Machine Learning. Vol, 11, pp. 11.
Kunang, Y. N., Nurmaini, S., Stiawan, D. & Suprapto, B. Y. (2021). Attack Classification of an Intrusion Detection System Using Deep Learning and Hyperparameter Optimization. Journal Of Information Security And Applications, 58, pp. 102804.
Lakhani, S., Yadav, A. & Singh, V. (2022). Detecting SQL Injection Attack Using Natural Language Processing. In 2022 IEEE 9th Uttar Pradesh Section International Conference On Electrical, Electronics And Computer Engineering (UPCON), pp. 1–5.
Li, Q., Li, W., Wang, J. & Cheng, M. (2019). A SQL Injection Detection Method Based on Adaptive Deep Forest. IEEE Access, 7, pp. 145385–145394.
Luo, A., Huang, W. & Fan, W. (2019). A CNN-Based Approach to The Detection of SQL Injection Attacks. In 2019 IEEE/ACIS 18th International Conference on Computer and Information Science (ICIS), pp. 320–324.
Mcivera, A., Rabehajaa, T., Wenb, R. & Morganb, C. (2017). Journal of Information Security and Applications.
Mondal, B., Banerjee, A. & Gupta, S. (2022). A Review of SQLI Detection Strategies Using Machine Learning. Machine Learning, 6(S2), pp. 9664–9677.
Nasereddin, M., Alkhamaiseh, A., Qasaimeh, M. & Al-Qassas, R. (2023). A Systematic Review of Detection And Prevention Techniques of SQL Injection Attacks. Information Security Journal: A Global Perspective, 32(4), pp. 252–265.
Natarajan, Y., Karthikeyan, B., Wadhwa, G., Srinivasan, S. & Akilesh, A. P. (2022). A Deep Learning Based Natural Language Processing Approach for Detecting SQL Injection Attack. In International Conference on Intelligent Systems Design and Applications, pp. 396–406.
Padmaja, B., Sekhar, G. C., Rama Padmaja, C. V., Chandana, P. & Krishna Rao Patro, E. (2022). Tool-Based Prediction of SQL Injection Vulnerabilities and Attacks on Web Applications. In Communication, Software and Networks: Proceedings of India 2022, pp. 535–543. Springer.
Pinzon, C. I., De Paz, J. F., Herrero, A., Corchado, E., Bajo, J. & Corchado, J. M. (2013). Idmas-SQL: Intrusion Detection Based on MAS To Detect and Block SQL Injection Through Data Mining. Information Sciences, 231, pp. 15–31.
Praveen, S., Dcouth, A. & Mahesh, A. (2022). Nosql Injection Detection Using Supervised Text Classification. In 2022 2nd International Conference on Intelligent Technologies (CONIT), pp. 1–5.
Ross, K., Moh, M., Moh, T.-S. & Yao, J. (2018). Multi-Source Data Analysis And Evaluation Of Machine Learning Techniques For SQL Injection Detection. In Proceedings of The ACMSE 2018 Conference, pp. 1–8.
Roy, P., Kumar, R. & Rani, P. (2022). SQL Injection Attack Detection By Machine Learning Classifier. In 2022 International Conference on Applied Artificial Intelligence and Computing (ICAAIC), pp. 394–400.
Sadeeq, H. T. & Abdulazeez, A. M. (2023). Metaheuristics: A Review of Algorithms. International Journal of Online and Biomedical Engineering, 19(9).
Sadeeq, H. T., Ameen, S. Y. & Abdulazeez, A. M. (2022). Cancer Diagnosis Based On Artificial Intelligence, Machine Learning, And Deep Learning. In 2022 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT), pp. 656–661.
Saleem, S., Sheeraz, M., Hanif, M. & Farooq, U. (2020). Web Server Attack Detection Using Machine Learning. In 2020 International Conference on Cyber Warfare and Security (ICCWS), pp. 1–7.
Salih, A. A. & Abdulazeez, A. M. (2021). Evaluation Of Classification Algorithms For Intrusion Detection System: A Review. Journal Of Soft Computing and Data Mining, 2(1), pp. 31–40.
Saran, M., Yadav, R. K., Maurya, P., Devi, S. & Tripathi, U. N. (2022). A Comprehensive Review For Detection And Prevention Techniques For SQL Injection Attack In Cloud Computing. International Journal of Innovative Research In Engineering and Management, 9(5), pp. 11–17.
Singh, G., Kant, D., Gangwar, U. & Singh, A. P. (2015). Sql Injection Detection And Correction Using Machine Learning Techniques. In Emerging ICT For Bridging The Future-Proceedings Of The 49th Annual Convention of The Computer Society Of India (CSI) Volume 1, pp. 435–442.
Sivasangari, A., Jyotsna, J. & Pravalika, K. (2021). SQL Injection Attack Detection Using Machine Learning Algorithm. In 2021 5th International Conference on Trends in Electronics And Informatics (ICOEI), pp. 1166–1169.
Sommervoll, Å. Å., Erd\Hodi, L. & Zennaro, F. M. (2023). Simulating All Archetypes Of SQL Injection Vulnerability Exploitation Using Reinforcement Learning Agents. International Journal of Information Security, pp. 1–22.
Tang, P., Qiu, W., Huang, Z., Lian, H. & Liu, G. (2020). Detection of SQL Injection Based on Artificial Neural Network. Knowledge-Based Systems, 190, pp. 105528.
Tasevski, I. & Jakimoski, K. (2020). Overview Of Sql Injection Defense Mechanisms. In 2020 28th Telecommunications Forum (TELFOR).
Tripathy, D., Gohil, R. & Halabi, T. (2020). Detecting SQL Injection Attacks In Cloud Saas Using Machine Learning. In 2020 IEEE 6th Intl Conference on Big Data Security on Cloud (Bigdatasecurity), IEEE Intl Conference on High Performance and Smart Computing,(HPSC) and IEEE Intl Conference on Intelligent Data and Security (IDS), pp. 145–150.
Xie, X., Ren, C., Fu, Y., Xu, J. & Guo, J. (2019). Sql Injection Detection For Web Applications Based On Elastic-Pooling Cnn. IEEE Access, 7, pp. 151475–151481.
Zhang, K. (2019). A Machine Learning Based Approach To Identify SQL Injection Vulnerabilities. In 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1286–1288.
Zhang, T. & Guo, X. (2020). Research On SQL Injection Vulnerabilities And Its Detection Methods. In 2020 4th Annual International Conference on Data Science and Business Analytics (ICDSBA), pp. 251–254.
Zhumabekova, A., Matson, E. T., Karyukin, V., Zhumabekova, K., Zhuandykov, B., Ussatova, O. & Telbayeva, T. (2023). Determining Web Application Vulnerabilities Using Machine Learning Methods. In 2023 19th International Asian School-Seminar on Optimization Problems of Complex Systems (OPCS), pp. 136–139.
Zolanvari, M., Teixeira, M. A., Gupta, L., Khan, K. M. & Jain, R. (2019). Machine Learning-Based Network Vulnerability Analysis of Industrial Internet of Things. IEEE Internet of Things Journal, 6(4), pp. 6822–6834.
Published
2024-04-25
How to Cite
[1]
H. Salih Abdullah and A. Mohsin Abdulazeez, “Detection of SQL Injection Attacks Based on Supervised Machine Learning Algorithms: A Review”, INJIISCOM, vol. 5, no. 2, pp. 152-165, Apr. 2024.
Section
Articles
