Persuasive cued click-points: Design, implementation, and evaluation of a knowledge-based “Graphical password authentication using cued click points. Request PDF on ResearchGate | Graphical Password Authentication Using Cued Click Points | We propose and examine the usability and. Cued Click Points Password Authentication using Picture Grids. Article (PDF . new click-based graphical password scheme called Cued.
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The area around an original click pasaword accepted as correct since it is unrealistic to expect user to accurately target an exact pixel. Given that hotspots and click-point clustering are significantly less prominent for PCCP than for CCP and PassPoints, guessing attacks based on these characteristics are less likely to succeed.
Attacks can target a vraphical account, or can try guessing passwords on a large number of accounts in hopes of breaking into any of them. Later when the tolerance limit was passworc to 3 only three of the eight participants were able to log in and when the tolerance limit was reduced to 2 only 2 of the participants was able to log in. In this paper also analyse the efficiency of tolerance value and security rate.
Graphical Password Authentication Using Cued Click Points
Attackers who gain knowledge of these hotspots through harvesting sample passwords or through automated image processing techniques can build attack dictionaries and more successfully guess PassPoints passwords . Once user completes all the user details uisng proceed to next stage, which is selecting click points on generated images, which ranges from This design decision had the effect of deemphasizing the edges of the image, slightly favoring the central area.
Malware is a major concern for text and graphical passwords, since key logger, mouse logger, and screen scraper malware could send captured data remotely or otherwise make it available to an attacker. During each trial, participants answered Likert-scale questions correspond to those reported in the previously cited studies A Likert scale is a psychometric scale commonly involved in research that employs grsphical. An online attack could be thwarted by limiting the number of incorrect guesses per account.
The task of selecting weak passwords which are easy for attackers to guess is more tedious, avoids users from making such choices.
In recognition based,a user is presented with a set of images and the user passes the authentication by recognizing and identifying the images he selected during the registration stage.
After done with all these above procedure, user profile vector will be created. In general, graphical passwords techniques are classified into two main categories: Those who barely shuffled selected their click point by focusing on the section of the image displayed in the viewport, while those who shuffled a lot scanned the entire image, selected their click-point, and then proceeded to shuffle until the viewport reached that area. This password authentication system allows user choice while influencing users towards stronger passwords.
This project is based on recall based Technique. When logging on, seeing an image they do not recognize alerts users that their previous click-point was incorrect and users may restart password entry. Those who shuffled a lot felt that the viewport hindered their ability to select the most obvious click-point on an image and that they had to shuffle repeatedly in order to reach this desired point.
As detailed in the next section, our proposed system accomplishes this by making the task of selecting a weak password more tedious and time-consuming. When user entered the all user details in registration phase, these user registration data stored in data base and used during login phase for verification.
Fewer shuffles lead to more randomization of click-points across users. In effect, this authentication schemes makes choosing a more secure password the path-of-least-resistance.
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When questioned, participants who barely shuffled said they felt that the viewport made it easier to select a secure click point. Among which the participant needs to click on any one point of his choice to make it a click point in the series. CCP also provides implicit feedback claimed to be uxing only to legitimate users.
PassPoints passwords from a small number of users can be used  to determine likely hotspots on an ckick, which can then be used to form an attack dictionary. In this paper focuses on the integrated evaluation of the Persuasive Cued Click Points graphical password authentication system, including usability and security.
The size of the fovea limits foveal vision to an angle of approximately 1 degree within the direct line to the target of interest. Persuasive Technology was first articulated by Fogg  as using technology to motivate and influence people to behave in a desired manner. Users tend to choose memorable passwords that are easy for attackers to guess, but strong system assigned passwords are cliick for users to remember.
GRAPHICAL PASSWORD AUTHENTICATION USING PERSUASIVE CUED CLICK POINT
After done with all these above procedure, user profile vector will be opened. The click-point distribution across users will be cuec randomly dispersed and will not form new hotspots. The scale is named after its inventor, psychologist RensisLikert. Jsing successful guessing attacks, attackers are able to either exhaustively search through the entire theoretical password space, or predict higher probability passwords i.
Though, users have difficulty remembering a password that is long and random-appearing.
For systems authdntication PCCP, CCP, and PassPoints and many other knowledge-based authentication schemescapturing one login instance allows fraudulent access by a simple replay attack. They either consistently shuffled a lot at each trial or barely shuffled during the entire session. In picture selection phase user select any image as passwords and consist of a sequence of five click-points on a given image.
The approach has proven effective at reducing the formation of hotspots, avoid shoulder surfing problem and also provide high security success rate, while still maintaining usability. A considerably more complicated alternative is to make user input invisible to cameras, for example, by using eye tracking as an input mechanism.