Software As Service Attack Detection and Prevention for Deceitful QR code


  • Manushree Sahay Bharati Vidyapeeth (Deemed to be University) College of engineering Pune.
  • Sandeep Vanjale Bharati Vidyapeeth (Deemed to be University) College of Engineering,Pune-43.
  • Madhavi Mane Assistant Professor, Dept. of Computer Engineering, Bharati Vidyapeeth (Deemed to be University) College of Engineering, Pune


Security analysis, Access control, Emotion interaction, identity authentication, social robot


The economic benefits and cons of using SaaS (software as a service) are not without debate. The security risks associated with SaaS prevent some consumers and service providers from using it. Using examples including software-defined networking, cloud computing, mobile cloud computing, and the Internet of Things, this article highlights the flexibility and use of SaaS in a number of contexts. Data security, application security, and SaaS deployment security are only some of the SaaS security challenges that will be investigated next. After that, options on how to make a SaaS platform more secure are presented, including how they may work together. The most severe security hole in the SaaS program is the SQL injection attack. The loss of private or crucial information might arise from this. (e.g., financial, personal). Both material (such as data) and intangible (such as reputation) assets might be jeopardized by these types of attacks on a company or organization's sensitive information. The goal of this study is to see whether it is possible to use machine learning methods to identify SQL injections in applications. To prepare the classifiers used in the evaluation procedures, both malicious and safe payloads were used. When given a payload, they can determine whether it contains malicious code. The purpose of this research is to identify malicious actions in a cloud-based SaaS setting. The latest studies on the practicality and safety of QR codes are thoroughly analyzed in the anti-phishing recommendations for this method, known as a secure QR code. We found the most common use cases and the most common attack vectors to exploit them. We did a massive literature search to accomplish this. The most often reported kind of fraud using QR codes as an attack vector is social engineering, sometimes known as phishing. QR codes on cellphones have exploded in popularity in assembly lines for automobiles.


Download data is not yet available.


Hlaing, Zar Chi Su Su, and Myo Khaing. "A detection and prevention technique on sql injection attacks." 2020 IEEE Conference on Computer Applications (ICCA). IEEE, 2020.

Chowdhury, Shreya, et al. "A Comprehensive Survey for Detection and Prevention of SQL Injection." 2021 7th International Conference on Advanced Computing and Communication Systems (ICACCS). Vol. 1. IEEE, 2021.

Ismail, Safwati, Mohammed Hazim Alkawaz, and Alvin Ebenazer Kumar. "Quick response code validation and phishing detection tool." 2021 IEEE 11th IEEE Symposium on Computer Applications & Industrial Electronics (ISCAIE). IEEE, 2021.

Jemal, Ines, et al. "Sql injection attack detection and prevention techniques using machine learning." International Journal of Applied Engineering Research 15.6 (2020): 569-580.

Chen, Ding, et al. "Sql injection attack detection and prevention techniques using deep learning." Journal of Physics: Conference Series. Vol. 1757. No. 1. IOP Publishing, 2021.

Bhoskar, Nikita, et al. "A Survey on Secrete Communication through QR Code Steganography for Military Application." Int. J. Res. Appl. Sci. Eng. Technol 10.1 (2022): 728-731.

Subairu, Sikiru, et al. "A Review of Detection Methodologies for Quick Response code Phishing Attacks." 2020 2nd International Conference on Computer and Information Sciences (ICCIS). IEEE, 2020.

Wahsheh, Heider AM, and Mohammed S. Al-Zahrani. "Secure real-time computational intelligence system against malicious QR code links." International Journal of Computers, Communications and Control 16.3 (2021).

Hu, Jianwei, Wei Zhao, and Yanpeng Cui. "A survey on sql injection attacks, detection and prevention." Proceedings of the 2020 12th International Conference on Machine Learning and Computing. 2020.

Arock, Michael. "Efficient Detection Of SQL Injection Attack (SQLIA) Using Pattern-based Neural Network Model." 2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS). IEEE, 2021

Ravi, C., Yasmeen, Y., Masthan, K. ., Tulasi, R. ., Sriveni, D. ., & Shajahan, P. . (2023). A Novel Machine Learning Framework for Tracing Covid Contact Details by Using Time Series Locational data & Prediction Techniques. International Journal on Recent and Innovation Trends in Computing and Communication, 11(2s), 204–211.

Prof. Nitin Sherje. (2017). Phase Shifters with Tunable Reflective Method Using Inductive Coupled Lines. International Journal of New Practices in Management and Engineering, 6(01), 08 - 13. Retrieved from

Kathole, A.B., Katti, J., Dhabliya, D., Deshpande, V., Rajawat, A.S., Goyal, S.B., Raboaca, M.S., Mihaltan, T.C., Verma, C., Suciu, G. Energy-Aware UAV Based on Blockchain Model Using IoE Application in 6G Network-Driven Cybertwin (2022) Energies, 15 (21), art. no. 8304.




How to Cite

Sahay, M. ., Vanjale, S. ., & Mane, M. . (2023). Software As Service Attack Detection and Prevention for Deceitful QR code. International Journal of Intelligent Systems and Applications in Engineering, 12(4s), 454–462. Retrieved from



Research Article

Similar Articles

You may also start an advanced similarity search for this article.