Abstract
Recently, hackers intend to reproduce malicious links utilizing several ways to mislead users. They try to control victims’ machines or get their data remotely by gaining access to private information they use via cyberspace. QR codes are two-dimensional barcodes with the capacity to encode various data types and can be viewed by digital devices, such as smartphones. However, there is no approved protocol in QR code generation; therefore, QR codes might be exposed to several questionable attacks. QR code attacks might be perpetrated using barcodes, and there are some security countermeasures. Some of these solutions are restricted to malicious link detection techniques with knowledge of cryptographic methods. Therefore, this study aims to detect malicious links embedded in 1D (linear) and 2D (QR) codes. A cybercrime attack was proposed based on barcode counterfeiting that can be used to perform online attacks. A dataset of 100000 malicious and benign URLs was created via several resources, and their lexical features were obtained. Analyses were conducted to illustrate how different features and users deal with online barcode content. Several artificial intelligence models were implemented. A decision tree classifier was identified as the most suitable model for identifying malicious URLs. Our outcomes suggested that a secure artificial intelligence barcode scanner (BarAI) is recommended to detect malicious barcode links with an accuracy of 90.243%.
Highlights
QR code is a machine-readable code consisting of an array of white and black squares, typically utilized for storing URLs or other information for viewing by several devices such as smartphones [1]. e retrieval of the data encoded in a QR code occurs within few seconds; thanks to the ultrahigh speeds used to verify the validity of the code received from the sensor [2]
Contributions. e contributions of this study are summarized as follows. (i) We explore a type of barcode-in-barcode attack based on QR code counterfeiting that can be used to perform online attacks. (ii) We conducted tests that show how different factors such as size and distance affect barcode scanning. (iii) We built an Artificial Intelligence (AI) model to detect malicious URLs encoded in barcodes based on the URL lexical properties. (iv) We applied several AI classifiers and compared them. (v) We developed BarAI based on the best model against malicious QR code links and analyzed the comparison results
Five AI classifiers were applied, naive Bayes (NB), support vector machine (SVM), logistic regression (LR), K-Nearest Neighbors (K-NN), and decision tree J48 (DT). e outcomes showed that the DT classifier is the most suitable model for recognizing QR code malicious links
Summary
QR code is a machine-readable code consisting of an array of white and black squares, typically utilized for storing URLs or other information for viewing by several devices such as smartphones [1]. e retrieval of the data encoded in a QR code occurs within few seconds; thanks to the ultrahigh speeds used to verify the validity of the code received from the sensor [2].Due to the high price of tags and identification devices, some researchers directed their attention to smartphones’ cameras as an alternative identification source such as fingerprints and barcodes [3, 4].When Denso Wave first invented the QR code in 1994, the main objective was to enable quick automobile scanning during manufacturing [1]. QR code is a machine-readable code consisting of an array of white and black squares, typically utilized for storing URLs or other information for viewing by several devices such as smartphones [1]. QR codes are widely used in much broader contexts, such as commercial tracking and mobile tagging. A QR code can include collecting data, sensing, and reading parameters from different environments [2]. QR codes were confirmed as an international standard in 2000 [5]. QR codes can store various information types, for instance, numeric (0–9), alphanumeric (letters and numerals), and binary data (0 and 1), as well as Kanji characters (Japanese writing) [7].
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