Transmitting Malware Through QR Codes: Risk Analysis and a Hybrid Detection Method

Mahesh Kumar Bagwani; Shubham Dwivedi; Vijay kumar; Srashti Jain; Pooja Koshti

doi:10.36948/ijfmr.2026.v08i03.79979

Transmitting Malware Through QR Codes: Risk Analysis and a Hybrid Detection Method

Author(s)	Mr. Mahesh Kumar Bagwani, Shubham Dwivedi, Vijay kumar, Srashti Jain, Pooja Koshti
Country	India
Abstract	QR codes have become deeply integrated into modern digital interactions, from payments and authentication topublic services. Their convenience, however, has created a newopportunity for cybercriminals. Malicious QR codes can redirect users to harmful domains, initiate unauthorized downloads, orharvest credentials through phishing websites. While QR codescanners are widely available, most lack robust mechanismsto detect malicious content hidden within a QR code. Thispaper presents a hybrid detection approach that combines theGoogle Safe Browsing API and PhishTank API with an additionallightweight machine-learning layer for URL risk estimation. Themachine-learning component analyzes lexical characteristics ofURLs to identify suspicious patterns that may not yet exist in threat databases. Experimental evaluation on a dataset of 500URLs demonstrates that the hybrid model enhances detectionaccuracy to 98 percent, reduces false positives, and improves resilience against zero-day attacks. The proposed method providesa more reliable and secure QR code scanning workflow suitablefor mobile and web applications.
Keywords	QR codes; phishing detection; hybrid security framework; malicious URLs; machine learning; threatintelligence APIs; zero-day attack detection.
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 8, Issue 3, May-June 2026
Published On	2026-06-01
DOI	https://doi.org/10.36948/ijfmr.2026.v08i03.79979

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Transmitting Malware Through QR Codes: Risk Analysis and a Hybrid Detection Method

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