Feasibility of Machine Learning-Enhanced Detection for QR Code Images in Email-based Threats

As QR codes become increasingly common in digital communication, cybercriminals have seized upon this technology as a vehicle for sophisticated URL-based email phishing attacks. These malicious QR codes, embedded within email messages, are designed to deceive recipients into revealing sensitive information. The primary challenge for cybersecurity vendors is efficiently detecting and analyzing these QR codes at scale, a task that is both computationally demanding and challenging within the high-throughput environment of email servers.This research investigates the application of convolutional neural networks (CNNs) to automate the detection of QR codes embedded in email images, addressing a growing vector for phishing attacks by integrating advanced image recognition techniques into existing email security frameworks. Through iterative development and refinement, a CNN model was designed to accurately differentiate between benign and malicious QR codes. The experimentation process revealed that while the model achieved high accuracy in early stages, it also encountered issues with overfitting as complexity increased, underscoring the need for careful balance in training processes.The study concludes with a proof of concept that demonstrates the effectiveness of CNNs in enhancing email security systems. It also emphasizes the importance of continuous model adaptation to address the evolving nature of phishing threats. This work represents a significant step toward scalable and efficient solutions for detecting QR code-based phishing attacks in the dynamic cybersecurity landscape.