Unified  URL  and QR  Based Phishing Detection Framework

Blessy, Kondaboina; Abhiram, Kannoju; Nikhil, Samboju; Sanjay, Guguloth

doi:https://doi.org/10.55041/ijcope.v2i4.015

Volume 02, Issue 04

Published on: April 2026

UNIFIED URL AND QR BASED PHISHING DETECTION FRAMEWORK

Kondaboina Blessy Kannoju Abhiram Samboju Nikhil Guguloth Sanjay

P Manoj Kumar

Department of CSE (Data Science) ACE Engineering College Hyderabad Telangana India

DOI:https://doi.org/10.55041/ijcope.v2i4.015

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Abstract

The rapid growth of digital communication and online transactions has significantly increased the risk of phishing attacks and fraudulent activities, particularly through malicious URLs and QR codes. Traditional security mechanisms, which rely primarily on static blacklists and manual verification, are often ineffective against newly generated or obfuscated threats. This paper presents a Hybrid Fraud Detection System that integrates machine learning techniques with rule-based analysis to identify and classify potentially malicious URLs and QR code-embedded links in real time. The proposed system extracts critical features from URLs, such as length, domain characteristics, and the presence of suspicious patterns, and processes them using supervised learning algorithms for accurate classification. Additionally, QR codes are decoded using image processing techniques, and the extracted URLs undergo the same detection pipeline. The system provides a risk assessment by categorizing links into different threat levels and generating confidence scores to assist users in decision-making. A web-based interface ensures accessibility and ease of use, while a backend architecture supports data processing, model prediction, and secure storage of analysis history. Experimental results demonstrate that the hybrid approach improves detection accuracy and reduces false positives compared to standalone methods. The system offers a scalable and efficient solution for modern cybersecurity challenges, contributing to proactive fraud prevention and enhanced user safety in digital environments.

How to Cite this Paper

Blessy, K., Abhiram, K., Nikhil, S. & Sanjay, G. (2026). Unified URL and QR Based Phishing Detection Framework. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(04). https://doi.org/10.55041/ijcope.v2i4.015

Blessy, Kondaboina, et al.. "Unified URL and QR Based Phishing Detection Framework." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 04, 2026, pp. . doi:https://doi.org/10.55041/ijcope.v2i4.015.

Blessy, Kondaboina,Kannoju Abhiram,Samboju Nikhil, and Guguloth Sanjay. "Unified URL and QR Based Phishing Detection Framework." International Journal of Creative and Open Research in Engineering and Management 02, no. 04 (2026). https://doi.org/https://doi.org/10.55041/ijcope.v2i4.015.

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References

[1] O. K. Sahingoz, E. Buber, C. Demirkol, and B. Diri, “Real‑Time Phishing Detection Based on Machine Learning Approaches,” IEEE Access, vol. 7, pp. 59064–59082, 2019, doi: 10.1109/ACCESS.2019.2906889

[2] S. Verma and S. Das, “Phishing URL Detection Using Machine Learning Techniques,” Procedia Computer Science, vol. 132, pp. 1063–1072, 2018, doi:10.1016/j.procs.2018.09.032.

[3] S. Marchal, T. Stateva, and M. Dacier, “PhishStorm: Detecting Phishing With Streaming Analytics,” IEEE Transactions on Network and Service Management, vol. 11, no. 4, pp. 427–440, 2014, doi:10.1109/TNSM.2014.2364674.

[4] C. Feng et al., “Neural Network‑Based Phishing Detection System,” Applied Sciences, vol. 9, no. 23, Art. no. 5076, 2019, doi: 10.3390/app9235076.

[5] S. U. Rehman et al., “Efficient Detection of Phishing Websites Using ML Algorithms,” Computers & Security, vol. 95, Art. no. 101857, 2020, doi:10.1016/j.cose.2020.101857.

[6] A. Kumar and R. Kumar, “Malicious URL Detection Using Machine Learning Techniques,” Journal of Intelligent & Fuzzy Systems, vol. 41, no. 4, pp. 4801–4814, 2021, doi:10.3233/JIFS-201492.

[7] A. Sakhare and B. Raman, “Phishing Detection Using Advanced AI Techniques,” Expert Systems with Applications, vol. 205, Art. no. 117622, 2022, doi:10.1016/j.eswa.2022.117622.

[8] T. Korkmaz et al., “URL‑Based Phishing Detection Using Feature Extraction,” Journal of Network and Computer Applications, vol. 159, Art. no. 102404, 2020, doi:10.1016/j.jnca.2020.102404.

[9] D. Sahoo, Q. M. Mahmood, and A. H. Shakil, “A Survey on Malicious URL Detection Using Machine Learning,” ACM Computing Surveys (CSUR), vol. 50, no. 6, Art. no. 84, 2017, doi:10.1145/3124374.

[10] P. Kieseberg, M. Mulazzani, M. Huber, and E. Weippl, “QR Code Security and Privacy Risks Analysis,” in Lecture Notes in Computer Science (LNCS), vol. 6402, Springer, pp. 95–108, 2010, doi:10.1007/978‑3‑642‑16252‑3_7.

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•Published on: Apr 04 2026

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