Abstract. The matrix barcodes known as Quick Response (QR) codes are rapidly becoming pervasive in urban environments around the world. QR codes are used to represent data, such as a web address, in a compact form that can be scanned readily and parsed by consumer mobile devices. They are popular with marketers because of their ease in deployment and use. However, this technology encourages mobile users to scan unauthenticated data from posters, billboards, stickers, and more, providing a new attack vector for miscreants. By positioning QR codes under false pretenses, attackers can entice users to scan the codes and subsequently visit malicious websites, install programs, or any other action the mobile device supports. We investigated the viability of QRcode-initiated phishing attacks, or QRishing, by conducting two experiments. In one experiment we visually monitored user interactions with QR codes; primarily to observe the proportion of users who scan a QR code but elect not to visit the associated website. In a second experiment, we distributed posters containing QR codes across 139 different locations to observe the broader application of QR codes for phishing. Over our four-week study, our disingenuous flyers were scanned by 225 individuals who subsequently visited the associated websites. Our survey results suggest that curiosity is the largest motivating factor for scanning QR codes. In our small surveillance experiment, we observed that 85% of those who scanned a QR code subsequently visited the associated URL.

Conventional planning of maintenance and renewal work for railway track is based on heuristics and simple scheduling. The railway industry is now collecting a large amount of data with the fast-paced development of sensor technologies. These data sets carry information about the conditions of various components in railway track. Since just before the beginning of the 21st century, data-driven models have been used in the predictive maintenance of railway track. This study presents a systematic literature review of data-driven models applied in the predictive maintenance of railway track. A taxonomy to classify the existing literature based on types of models and types of applications is provided. It is found that applying the deep learning methods, unsupervised methods, and ensemble methods are the new trends for predictive maintenance of railway track. Rail geometry irregularity, rail head defect, and missing rail components detection were the top three most commonly considered issues within the application of data-driven models. Prediction of rail breaks has received increasing attention in the last four years. Among these data-driven model applications, the collected data types are the most critical factors which affect selecting suitable models. Finally, this study discusses upcoming challenges in the predictive maintenance of railway track.

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