rahuls final ppt

Acharya Institute of TechnologyDepartment of ISEDepartment of ISE

Under the Guidance ofProf. Yogesh N

ByRahul K N

SEMINAR ONSECURING MOBILE CLOUD

USING FINGERPRINT

AUTHENTICATION

Acharya Institute of TechnologyDepartment of ISE

Contents Introduction Difference between Existing system

and Proposed system Proposed Solution Results and Discussion Features Applications Drawbacks Conclusion References


Introduction Mobile cloud computing = mobile computing + cloud computing It presents new issues of security threats

such as unauthorized access to resources exist in mobile cloud.

Here we’re using fingerprint recognition system to secure mobile cloud.


Difference between Existing System and Proposed SystemBasis Existing System

(Password)Proposed system (Fingerprint)

Security Less secure ,as the passwords can be reused ,phished or key logged.

More secure, as Fingerprints are unique and complex enough to provide a robust template for authentication.

Identification Accuracy

Cannot be identified accurately whether the one who is accessing the resources is authorized or not.

Authorized user is identified by his fingerprint hence there is no chance of unauthorized user to get access to the resources in cloud.

Protection from the attacks

Less protected ,as injection attacks on the database is possible .

More protected, as injection attacks on the database is impossible.

Others Remembering the passwords is difficult .

Remembering anything is not required as ultimately all the system requires is the image of your Fingertip.


Proposed system

Pre-processing -> convert to gray-scale ,edge enhancement, filtering, binarization, thinning, map direction, minutiae extraction

Core point detection

Input Finger image

Feature extraction

The User is Accepted

Matchig?

Data-base

enrollment

yes

no


EXPERIMENTAL RESULTS AND DISCUSSION

The tests are separated into two parts:1) Functionality 2)Performance

1. Evaluating Functionality. Figure 2 summarizes the output from various functions, including (a) the original image, (b) convert to gray-scale, (c) edge enhancement, (d)filtering, (e) binarization, (f) thinning, (g) map direction, and (h) minutiae extraction

Figure 2: Functions - Output for Galaxy Note


Algorithms Used for Matching

Relative Distance Matching Image Mapping Each algorithm having a different threshold score for matching.


Simple Algorithm• The similarity score (S) is the result of the comparison between the extracted features and features stored in a database.

If (S is low value) thenLittle similarityIf (S is high value) thenHigh similarity

•After that, the decision will be based on the similarity score (S), which is compared to a predefined threshold (T).

If (S > T) thenThe user is acceptedElse if (S < T)The user is rejected


2. Evaluating PerformanceIn this section, the process time is calculated for each function to test if the performance rate is acceptable according to the rates established by the National Institute of Standards and Technology (NIST). Figures 3,4 and 5 show the process time from testing the fingerprint images for the Sony Xperia S , Samsung Galaxy S3 and BlackBerry Z resp.


Figure 3 shows the experiment results of the fingerprint image taken with the Sony Xperia S

device, which recorded 0.9 seconds as the maximum time and 0.4 seconds as the average


Figure 4 illustrates the process time for an image taken with a Samsung Galaxy S3 device the

maximum time is 2.4 seconds and the average is 0.5 seconds.


Another example is the BlackBerry Z in Figure 5, with approximately 4 seconds as a maximum

time and 0.8 seconds as the average recorded time.


DiscussionFrom the experiment results, it is evident that, The range for the total processing time to pre-process a fingerprint image takes between 1 and 12 seconds. Table 1 summarizes the range of process times for each function in the pre-processing class. An acceptable enrolment time should be equal to or less than two minutes, which means that the enrolment process must be completed in 120 seconds. When the total process time is subtracted from the acceptable enrolment time, there are 108 seconds remaining for enrolment.


Figure 6 shows the process time of enrolment for six different mobile devices, and nearly all

are very similar.


Figure 7 illustrates that 0.2 seconds is the minimum time, 19 seconds is the maximum, and 0.4 seconds is the average. This means that the proposed approach

achieved 19 seconds, with 108 seconds being the maximum acceptable time.


The Matching Process time is shown in Figure 8 for three mobile devices. The average time is 0.4 seconds, which is an accepted rate.


Features The proposed solution is not only to secure unauthorized access, but also to protect databases from injection attacks due to the absence of string input from users. The fingerprint image is the only input from the user in accordance with the interface design. No other input is permitted from the user to enter the system. The interface in this solution is based on HTML5, which is a cross-platform and has been tested on different mobile platforms.


Applications

User doesn’t have to remember the password as the password would be user’s fingerprint.As the fingertip’s image is captured by mobile phone’s camera it is way cheaper than that of getting the fingerprint by using fingerprint scanner.It provides more security as compare to the traditional approaches of authentication. With the addition of some filters to segment the method in this solution, it will be able to work with web cameras.


Drawbacks

Due to the complicated background, hand and camera shaking during taking photos, the result of experiments indicates it is impossible to get a desirable performance of fingerprint recognition using mobile phone cameras in real-life scenarios even though it might be working well under laboratory environment.mobile phone cameras are mostly optimized to capture human face or other more “attracting” objects in a frame instead of fingerprints.


Conclusion

The focus in this research is on the mobile cloud and protecting mobile cloud resources from illegitimate access. The proposed solution for authenticating mobile cloud users using the existing mobile device camera as a fingerprint sensor to obtain a fingerprint image, and then process it and recognize it. Results show that the proposed solution has added value to keep performance at an accepted level.


References

• X. Li, "Cloud Computing: Introduction, Application and Security from Industry Perspectives,"International Journal of Computer Science and Network Security, vol. 11, pp. 224-228, 2011.

• F. Omri, R. Hamila, S. Foufou, and M. Jarraya, "Cloud-Ready Biometric System for Mobile Security Access," Networked Digital Technologies, pp. 192-200, 2012.

• M. O. Derawi, B. Yang, and C. Busch, "Fingerprint Recognition with Embedded Cameras on Mobile Phones," Security and Privacy in Mobile Information and Communication Systems, pp. 136-147,2012.

• H. T. Dinh, C. Lee, D. Niyato, and P. Wang, "A survey of mobile cloud computing architecture,applications, and approaches," Wireless Communications and Mobile Computing, 2011.

• R. Mueller and R. Sanchez-Reillo, "An Approach to Biometric Identity Management Using Low Cost Equipment," in Intelligent Information Hiding and Multimedia Signal Processing, 2009. IIH-MSP'09. Fifth International Conference on, 2009, pp. 1096-1100.• B. Y. Hiew, A. B. J. Teoh, and O. S. Yin, "A secure digital camera based fingerprint verificationsystem," Journal of Visual Communication and Image Representation, vol. 21, pp. 219-231, 2010.• B. Hiew, A. B. J. Teoh, and D. C. L. Ngo, "Pre-processing of fingerprint images captured with adigital camera," in Control, Automation, Robotics and Vision, 2006. ICARCV'06. 9th InternationalConference on, 2006, pp. 1-6.


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rahuls final ppt

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