01-11-2012, 05:10 PM
GRAPHICAL PASSWORD AUTHENTICATION USING CLICK CUED POINTS
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ABSTRACT
Click cued points is a click-based graphical password scheme, a cued-recall graphical password technique. Users
Click on one point per image for a sequence of images. The next image is based on the previous click-point. Performance was very good in terms of speed, accuracy, and number of errors. Users preferred CCP to PassPoint, saying that selecting and remembering only one point per image was easier, and that seeing each image triggered their memory of where the corresponding point was located. CCP also provides greater security than PassPoints because the number of images increases the workload for attackers.
Key words: Graphical Passwords, Computer Security, Authentication,
INTRODUCTION
Various graphical password schemes have been proposed as alternatives to text-based passwords. Research and experience have shown that text-based passwords are fraught with both usability and security problems that make them less than desirable solutions. Psychology studies have revealed that the human brain is better at recognizing and recalling images than text. Graphical passwords are intended to capitalize on this human characteristic in hopes that by reducing the memory burden on users, coupled with a larger full password space offered by images, more secure passwords can be produced and users will not resort to unsafe practices in order to cope.
In this project, we propose a new click-based graphical password scheme called Cued Click Points (CCP). It can be viewed as a combination of PassPoints , Passfaces , and Story . A password consists of one click-point per image for a sequence of images. The next image displayed is based on the previous click-point so users receive immediate implicit feedback as to whether they are on the correct path when logging in. CCP offers both improved usability and security.
Users could quickly create and re-enter their passwords. Another feature of ccp is the immediate implicit feedback telling the correct user whether their latest click-point was correctly entered.
Graphical Password Authentication Using Click Cued Points
Created by SUBHA A Page 9
OBJECTIVE
Cued Click Points (CCP) is a proposed alternative to PassPoints. In CCP, users click one point on each of images rather than on different points on one image. It offers cued-recall and introduces visual cues that instantly alert valid users if they have made a mistake when entering their latest click-point at which point they can cancel their attempt and retry from the beginning. It also makes attacks based on hotspot analysis more challenging.
As shown in Figure 1, each click results in showing a next-image, in effect leading users down a “path” as they click on their sequence of points. A wrong click leads down an incorrect path, with an explicit indication of authentication failure only after the final click. Users can choose their images only to the extent that their click-point dictates the next image. If they dislike the resulting images, they could create a new password involving different click-points to get different images.
System Study
Click cued points is the best graphical password authentication technique. It offers cued-recall and introduces visual cues that instantly alert valid users if they have made a mistake when entering their latest click-point at which point they can cancel their attempt and retry from the beginning. It also makes attacks
based on hotspot analysis more challenging.
Existing System:
The existing system is PassPoints . It proposed
Passwords which could be composed of several points anywhere on an
image. They also proposed a scheme with three overlapping grids, allowing for login attempts that were approximately correct to be
accepted.
Drawbacks in the existing systems:
It seems obvious that some areas of an image are more attractive to users as click-points. If this phenomenon is too strong, the likelihood that attackers can guess a password significantly increases. If attackers learn which images are being used, they can select a set of likely hotspots through image processing tools or by observing a small set of users on the target image and then building an attack dictionary based on those points.