![]() ![]() This paper presents a theoretical model of CRI to study this threat in a systematic manner. Over the period, a number of research articles have published with different techniques and procedures but have failed to detect all associated risks and provide a comprehensive solution. The identification of phishing techniques can be performed in various methods of communications like email, instant messages, pop-up messages, or at web page level. It is an unlawful activity which uses a group of social engineering and technology to collect an Internet user's sensitive information. Phishing is a rapidly growing threat in cyber world and causing billions of dollars in damage every year to internet users. A very accurate model wasĬreated, aiding in the reduction of quishing behaviour. Words were tokenized, and naive Bayesian machine learningĬlassification techniques were used in a recursive loopĪlongside logistic regression. Toĭistinguish between legitimate and phishing URLs, traits and Were part of the QR code were used to obtain features. Were extracted using a count vectorizer, and the URLs that ![]() Raised by the increasing use of QR codes. The necessity for experts in the field to solve security concerns Issues that might arise with QR codes and other information Of QR (Quick Response) codes is one of the rapidlyĮxpanding interface technologies. Information, like social media, might provide difficulties. Technologies like smartphones and new ways of disseminating Technologies provide both opportunities and challenges. For security experts,Įspecially those working in fields like digital forensics, new To accommodate new technologies and communication ![]()
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