Secure Real-Time Computational Intelligence System Against Malicious QR Code Links

Web attackers aim to propagate malicious links using various techniques deceive users. They attempt control victims’ devices or obtain their passwords remotely, thereby acquiring access bank accounts, financial transactions, private and sensitive information they trade via the Internet. QR codes are accessible, free, easy use, can be scanned through several free apps on smartphones. As there is no standard structure authentication phase in code generation, such vulnerable suspicious online content embedding, i.e., phishing, Cross-Site Scripting (XSS), malware. Many studies have highlighted attacks that may perpetrated barcodes, some security countermeasures. Several of these solutions limited link detection methods require knowledge cryptographic techniques. This study’s main objective detect URLs embedded codes. A dataset 90 000 benign was collected from resources, lexical properties were extracted. Two computational intelligence models, fuzzy logic multilayer perceptron artificial neural network (MLP-ANN), applied compared. An MLP-ANN identified as best classifier for detecting URLs, a proactive, secure, real-time barcode scanner implementation (BarCI ) against proposed based this classifier. The results demonstrate approach enables efficient attack with 82.9% accuracy