Open Access Open Access  Restricted Access Subscription or Fee Access

Biometric Security Technique in Cellular Phones for Secured Communication

K. Kavitha, S. Natarajan

Abstract


Mobile phones are playing a vital role in our life. Aswireless communication has been rapidly advanced, mobileapplications and services are becoming more and more popular, likeinstant messaging, downloading of a variety of contents, mobilecommerce, mobile banking, and information searches. Mobiledevices such as cellular phone, PDA and smart phone are exposed tonumerous security threats like malicious code (including virus, wormand Trojan horses), vulnerabilities of mobile phone, attacks on communication, data robbery and damage and mobile spam. Forvoice communications, the introduction of security has been significantly slower. The main reason has been technologicallimitations.The current protocols for wireless communication for eitherplacing or receiving a call requires both MIN (Mobile IdentificationNumber) and ESN (Electronic Serial Numbers) to be broadcastedbetween Wireless telephone and MSC (Mobile Switching Center).While doing so it is important to protect the Pair MIN and ESN,because they can be easily intercepted and can be illegallyprogrammed. So we need a strong authentication mechanism toprotect the pair (MIN, ESN) and need an improved security system toprotect against unauthorized use of wireless communications.The paperemploys the user’s finger print to authenticate theagents of wireless communication. By using finger prints it guarantees a strong authentication mechanism. Also it avoidsmaximum fraudulent case. The proposed system involves using atoken generated from biometric information, the user’s personalfingerprint as the secret key in the context of a “challenge-response”scenario. This system includes Finger print capture Module and acentral authentication system coupled to a Mobile Switching Centre.The Finger Print Capture Module uses the “Minutia Algorithm” andthe “Pattern Matching Algorithm” to record and retrieve the fingerprints.When a communication is initiated the Central Authenticationsystem engages the “Challenge-response” authentication. It checkswhether the user’s fingerprint entered through the FingerprintCapture Module matches with the information sent from the CentralAuthentication System. Only if it matches the call is connectedotherwise the call is denied. This virtually eliminates all, of thedrawbacks of the existing system.


Keywords


Central Authentication System, Electronic Serial Number, Fingerprint Capturing Device, Mobile Identification Number, Mobile Switching Center

Full Text:

PDF

References


R. Cappelli, A. Lumini, D. Maio, and D. Maltoni, “Fingerprint

classification by directional image partitioning”, IEEE Trans. on Pattern

Analysis and Machine Intelligence, 35:1209–1223, 2002.

Jin-Hyuk Hong, Jun-Ki Min, Ung-Keun Cho, Sung-Bae Cho,

“Fingerprint classification using one-vs-all support vector machines

dynamically ordered with naïve Bayes classifiers”, Pattern Recognition,

Published by Elsevier Ltd. 41, 662 – 671, 2008.

Jun-Ki Min, Jin-Hyuk Hong, Sung-Bae Cho, “Fingerprint classification

based on subclass analysis using multiple templates of support vector

machines”, Intelligent Data Analysis, Volume 14, pp 369-384, 2010.

J. Chang and K. Fan. A new model for fingerprint classification by ridge

distribution sequences. Pattern Recognition, vol 35, pp: 1209–1223,

Xiaoguang He, Jie Tian, Liang Li, Yuliang He and Xin Yang,

“Modeling and Analysis of Local Comprehensive Minutia Relation for

Fingerprint Matching” , IEEE Transactions on Systems, Man, and

Cybernetics, Part B: Cybernetics, vol 37, pp: 1204 – 1211, 2007.

Monowar Hussain Bhuyan, Sarat Saharia and Dhruba Kr Bhattacharyya,

“An Effective Method for Fingerprint Classification”, International Arab

Journal of e-Technology, Vol. 1, No. 3, January 2010.

Xudong Jiang, Manhua Liu and A.C. Kot, “Fingerprint Retrieval for

Identification”, IEEE Transactions on Information Forensics and

Security, vol 1, pp 532 – 542, 2006.

Mehran Yazdi and Kazem Gheysari, “A New Approach for the

Fingerprint Classification Based on Gray-Level Co-Occurrence Matrix”,

World Academy of Science, Engineering and Technology, 2008.

Shah Shesha and Sastry P. S., “Fingerprint Classification Using a

Feedback-Based Line Detector”, IEEE Transactions on Systems, Man,

and Cybernetics, Part B: Cybernetics, vol. 34, no. 1, pp. 85- 94, 2004.

Emmanuel Opara, Mohammad Rob, Vance Etnyre “Biometric And

Systems Security: An Overview of End-To-End Security System”,

Communications of the IIMA 2006 Volume 6 Issue 2.

Vincenzo Piuri, Fellow, IEEE, Fabio Scotti, Member, IEEE,

“Fingerprint Biometrics via Low-cost Sensors and Webcams”, 2008

IEEE.

C-C. Han, H-L. Cheng, C-L. Lin, K-C. Fan, “Personal Authentication

Using Palmprint Features”, Pattern Recognition 36, 2003, pp. 371-381.

S. Kamara, B. de Medeiros, and S. Wetzel, “Secret locking: Exploring

new approaches to biometric key encapsulation”, Proceedings of ICETE,

pp. 254-261, 2005.

X. Wu, N. Qi, K. Wang, and D. Zhang, “A Novel Cryptosystem based

on Iris Key Generation”, Fourth International Conference on Natural

Computation (ICNC'08), pp. 53-56, 2008.

A.Teoh and J. Kim, “Secure biometric template protection in fuzzy

commitment scheme”, IEICE Electron. Express, vol. 4, no. 23, pp. 724-

, 2007.


Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.