An Intelligent Security Model for QR Code Threat Mitigration

Ms. Y .Vaishnavi 1
Asst. Professor, Department of AIDSAnnamacharya Institute ofTechnology and Sciences,
Tirupati – 517520, A.P.vaishnavireddy0503@gmail.com
K. Venkat Sai 4
Student, Department of AIDSAnnamacharya Institute ofTechnology and Sciences,
Tirupati – 517520, A.P.venkatsaikande11@gmail.com
M. Rahul Reddy 2
Student, Department of AIDSAnnamacharya Institute ofTechnology and Sciences,
Tirupati – 517520, A.P.rahulreddym293@gmail.com
B.Ramesh 5
Student, Department of AIDSAnnamacharya Institute ofTechnology and Sciences,
Tirupati – 517520, A.P.b.ramesh868829@gmail.com
B. Siva Joshna 3
Student, Department of AIDSAnnamacharya Institute ofTechnology and Sciences,
Tirupati – 517520, A.P.sivajoshna694@gmail.com

Abstract:QR codes are widely used for digital interactions, from online payments to information sharing, due to their convenience and speed. However, this popularity has made them a target for cyberattacks, such as phishing, malware distribution, and unauthorized data collection.Traditional security measures like URL blacklists are often insufficient against rapidly evolving threats. This research proposes an AI-driven framework to secure QR codeinfrastructure by detecting malicious activity in real time.  Using a combination of machine learning and deeplearning techniques, the system can analyze QR code content, linked URLs, and structural features to identify anomalies, enhancing user safety and trust in digital services.The proposed solution integrates predictive models for classification of QR codes as safe or harmfuland incorporates real-time alerts to prevent malicious interactions.Index Keywords: QR Code Security, Malicious QR Detection, AI, Machine Learning, Deep Learning,Cybersecurity, Phishing Prevention, Real-Time Threat  Detection, QR Code Analysis, Predictive Modeling