# Investigation of Using CAPTCHA Keystroke Dynamics to Enhance the Prevention of Phishing Attacks
## 2 Related Work and Background
The proposed methodology was implemented by using Matlab without investigating the effectiveness of the approach in a real experiment. In contrast, the other study used a cropped image CAPTCHA (CIC) algorithm, where the user undertook two phases: registration and log-in. In the registration phase, the user was required to enter all details, select two images from among 80 images that were randomly generated by the server, and perform a cropping process based on predefined constraints. The system subsequently created a user profile containing registration details and the cropped pieces, which would then be sent server-side for storage in a database. During the log-in phase, after entering their details, the users could determine whether a suspicious webpage was legitimate or a phishing website according to the image received in this phase. If the user received any of the images selected in the registration phase, along with nine random images, the suspicious webpage could be identified as legitimate. Otherwise, it would be recognised as a phishing website. If a webpage was determined as legitimate, the user would have to perform the same cropping procedure as for registration. To evaluate the proposed system, the authors recruited 114 users, all familiar with the Internet and computers. The authors used three verification keys, which were password, PIN, and the proposed method, to evaluate the user performance over three months. The results indicate that the CIC success rate was higher than a password at 9% and higher than a PIN at 1% in the third month. In addition, some demographic data were collected in the survey to evaluate the proposed approach in terms of ease to remember and response time. According to the results, 82% of the participants believed CIC was easier to use, while 11% believed CIC was slow to solve. Moreover, the study by the authors of Ref. [28] proposed a method to distinguish between authorized and unauthorized users by combining CAPTCHA attributes and human capabilities called bio-detection functionalities (BDF). The authors concluded that a CAPTCHA could help protect against third-party attacks when it is embedded with mechanisms (i.e., a BDF mechanism) that help identify the user. Furthermore, in Ref. [29], it was highlighted how behavioural biometric authentication could be used to prevent phishing. The above author discussed how keystroke dynamics could be used to prevent phishing attacks, as well as ensuring that phishers do not impersonate users. However, this idea was merely underlined without proving the concept in an experimental study. The use of the keystroke dynamics technique is a promising trend in identifying users from typing patterns. In the early 1980s, both the National Science Foundation (NSF) and National Institute of Standards and Technology (NIST) in the US found in studies that typing patterns were unique and recognisable [30]. These initial promising results prompted a large number of researchers to explore keystroke dynamics, whereupon new methods were developed to improve the efficiency of using keystroke dynamics in user authentication, achieved by measuring and testing users' typing patterns. In fact, all research in this area has sought to provide efficient solutions that will provide a robust and inexpensive authentication mechanism. In particular, a study [31] examined the use of free text, applying Euclidean principles to obtain the distances between key pairs according to their position on the keyboard. An interesting finding of this work, which relates closely to the present research, is that flight time is more relevant than dwell time. This finding is evaluated in the current study. The best results were obtained with the features, down-down (DD), up-down (UD), and up-up (UU), producing a 21% false acceptance rate (FAR) and 17% false rejection rate (FRR). In addition, two studies [32; 33] investigated a free-text keystroke dynamic authentication approach for English and Arabic input.