Security improvement of credit card online purchasing system
The paper aims to improve the security of the credit card online purchasing with taking into account the time and cost issues. Since the current online purchasing system using credit card has security drawbacks, a security improvement is suggested in this work by implementing a model which integrate...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Academic Journals
2011
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/6387/ http://irep.iium.edu.my/6387/ http://irep.iium.edu.my/6387/1/Security_improvement_of_credit_card_online_purchasing_system.pdf |
| Summary: | The paper aims to improve the security of the credit card online purchasing with taking into account the time and cost issues. Since the current online purchasing system using credit card has security drawbacks, a security improvement is suggested in this work by implementing a model which integrates the current authentication system of credit card with the fingerprint authentication. Moreover, it complements with a technique for validating and transmitting the fingerprint features. The customer submits his or her credit card information through the internet together with a file containing the fingerprint features and a validation code. This technique makes the customer feels more secure, at the same time it makes credit card fraud more difficult. Credit card information, fingerprint transaction authorization code and fingerprint features were the main components of the model. The authorization code is able to handle the usage of the scanned fingerprint features for one time only and preventing the submission of old and expired features. In addition, a ‘biometric and authorization code’ file is presented in this work to increase the fingerprint features security. It has its own structure in terms of storing the authorization code and fingerprint features which is unknown for the attacker and known only for the matching program. The average processing time consumed by the model to match all the data is 2.47 s while the overall accuracy rate was 99.48% with 0.52% error rate. |
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