Security Issues in Smart Infrastructures for MMBS of Wireless s for ATM banking S.Koteswari #1, Dr.P.John Paul *2,S.Indrani ~3 # 1 Associate. Professor, Dept of ECE, D.N.R Engineering College, Bhimavaram,W.G.Dist, Andhra Pradesh. India. email:eshwari.ngr@gmail.com * 2 Professor, Dept of CSE, GATES Engineering College, Gooty, Ananthapur, Andhra Pradesh, India. email:jppulipati@yahoo.com ~ 3 Assistant Professor, Dept of CSE, Adarsh College, Bangalore, India email: indu2884@gmail.com Abstract: Greater demand for fast and accurate user identification and authentication has increased the growth in electronic transactions. Personal identification numbers (PIN s) for identification and security clearances are often used as access codes for buildings, banks accounts and computer systems. Conventional method of identification based on possession of ID cards or exclusive knowledge like a social security number or a password are not all together reliable. A multimodal biometric authentication scheme for automated teller machine (ATM) banking systems is proposed in this paper. In this scheme, a fingerprint and iris biometric techniques are fused with the ATM for person enrollment and authentication to ameliorate the security level and Visual cryptography technique is used to increase the security. Discrete Cosine transform (DCT) is usually used in JPEG based image transform coding, but this paper presents DCT_DHT (Discrete Hartley Transform) transform for feature extraction. This paper also presents VLSI architecture of enrollment and authentication module for ATM banking, which is suitable of achieving low hardware circuit cost as well as low power consumption. This is demonstrated through a Xilinx FPGA, vertex-6 family and XC6VLX75T device. text,image,graphics,speech,audio,vedio,animatio n,handwriting,datafiles.the role of WMMC is to receive multimedia content from the multimedia distribution server and displayed to the user, where in the content may be live or on the demand. Different trends towards multimedia communications are represented in figure1.the need for providing a variety of multimedia communication services is becoming not only technical, but also economically feasible. WMMC requires bit rate channels of medium to high (64kb/s to 2Mb/s per user). Even with scalable compression schemes there is chance of loss of data on wireless system. The following issues need to considered i.e. Firstly different methods to increase the capacity of wireless channels, and secondly to provide quality of service (QoS) in a cost effective way and thirdly the different efficient procedures to combine the wireless channel impairments. Where in the human beings complex perception, communicating behaviors and the ways of acting suits best the multimedia. The multimedia includes audio, video and data and the applications such as in education, travel, banking insurance, administration and publishing are emerging at a fast pace and are need to be characterized by large multimedia documents and that must be communicated within very short delay. Keywords:ATM,DCT_DHT,security,Visual Cryptography,wireless communication. I.INTRODUCTION Motivation for Wireless Multimedia Services and Technologies: Wireless multimedia communication (WMMC)is the field referring to the representation, storage, retrieval of the information expressed in none other than the multiple media such as the Figure 1: Trends in Multimedia Communication
There are two key modes in the wireless multimedia systems, they are (1) Person to person communication. (2) Person to machine communication. The key requirements of these two modes are like instant availability[1,2], services from any terminal (mobile point of delivery), real time information transfer, service always on-line. With respect to the service offered to them by the communication system, multimedia applications have several requirements based on different technologies. This requirement which influences the criticality of the demands,that depends on the usage and type of the application gives to Universal multimedia access (UMA). UMA under different network conditions deals with the delivery of images, video, audio and multimedia content. With the limited communication, that is a need to enable the terminals is the main motivation behind UMA. Future services independently on their choice of access technology, usage preferences and terminal equipment are accessed by the users which are enabled by the UMA. Wireless multimedia services and technologies will perform effectively with the quality of service (QoS) and authentication, authorization and accounting of the end-to-end network to verify the users identities and rights and also to ensure the requirements of QoS, which provides a end-toend seamless service to the user. II. BACK GROUND 2.1. Multimodal biometrics & Security Any physical and /or behavioral characteristics of human can be considered as a Biometrics and the characteristics of biometrics include the universality, uniqueness, permanence, measurability, performance, acceptability, circumvention. In this paper, the main goal of accurate identification is to prevent the imposter accessing the secured application of ATM banking[1]. There are three ways in which users can be identified such as, something the user knows(passwords, PIN)[3], the user has (Key,Cards and Tokens) and the user is (Unique Biological properties).the data stored in database can be easily lost, stolen, shared or manipulated and there by the need for security plays the main role or any biometrics. Multimodal Biometrics System (MMBS) can be defined as a recognition system which is capable of identifying a person based on their biological properties. The biological properties can be physical characteristics like face, palm print, iris, hand vein etc and behavior properties like speech, gait, signature etc. Thus the biometric system offers a natural and reliable solution to recognize the individual. Biometric systems that perform the identification of person based on the information obtained from multiple biometric traits are known as multimodal biometric system. Although there has been much research on combining different biometrics for variety of purposes, in this paper it is focused on the combination of fingerprint and iris,which are two of the characteristics that can reach the best recognition performance for high security application and security is provided for the database through visual cryptography and the main application of these multimodal biometric database is for ATM banking. A lot of research has been carried out in the field of authentication and key exchange protocols, which are based on passwords. The Password based user authentication systems are low cost and easy to use but however, the use of passwords has intrinsic weaknesses. The user chosen passwords are inherently weak since most users choose short and easy to remember passwords. In particular, passwords are normally drawn from a relatively small dictionary; thereby prone to Brute-force dictionary attacks, where an attacker enumerates every possible password in the dictionary to determine the actual password. The fusion of fingerprint and iris features for cryptographic key generation is proposed by A.Jagadeesan. The use of multimodal biometrics for key generation provides better security, as it is made difficult for an intruder to spool multiple biometric traits simultaneously. 2.2. Visual cryptography Security of data has been a major issue from many years. Using the age old technique of encryption and decryption has been easy to track for people around. Providing security to data using new technique is the need of the hour. This project uses the technique of Visual cryptography and providing biometric authentication. As computing power becomes more and more fast, our older cryptographic systems becoming less secure because an attacker can attempt larger number of random attack attempts in shorter time. Hence, there is the need for security of biometrics in which information security is one of the most important issues in growing information technology environment. The need for very efficient security systems for preventing confidential information
from being accessed by unauthorized persons is achieved through this approach of research. Our approach is presented for iris images and finger print images and it can also applicable to other biometrics such as facial images, using grayscale and natural images such as face, pictures, using more biometric samples into meaningful shares. Visual cryptography [12,13]is a simple and powerful method which can provide high security for confidential information. Concept of visual cryptography is introduced by Moni Naor and Adi Shamir in 1994 during EUROCRYPT 94[13]. The idea is to split a message into n different pieces such that the original message is visible if any k (or more) of them are used together, but totally invisible if fewer than k pieces are used for getting the message. In this method each message is considered as an image of black and white pixels. This image is divided into n slides called transparency. Each pixel of the message appears in each transparency in a different modified version. For getting the original information from transparencies, all of them are stacked together with proper alignment. The simplest example of visual cryptography is a scheme in which we split the image[14] into two different shares. The decryption of the image will be done by overlapping the shares. When we place both the shares one over another with proper alignment, we can interpret the original image. But in this paper we have used same key for the encryption and decryption, if there is any difference in the key the original image cannot be retrieved. Another advantage of this method is that it also increases the randomness in the decrypted image. The pixels in the encrypted image are scattered more randomly then the existing pixel sieve method. III.PROPOSED APPROACH In the proposed work, the multimodal biometric information is used for mutual authentication and key generation. The user authentication for ATM banking is shown in figure 2 below, which includes the iris and finger print for multimodal biometrics and the security is provided using visual cryptography. The use of multimodal biometrics for key generation provides better security, as it is made difficult for an intruder to spool multiple biometric traits simultaneously. In this paper VLSI architecture for the visual cryptography of the multimodal biometric is obtained which is used for ATM banking. An important factor which must be considered for iris recognition system is the effect of noise on its performance. The four kinds of noises that exist in a normalized iris image are eye lids, eyelashes, pupil and reflection noises. In this paper, a complete solution for compensating these types of noises should be implemented to achieve higher accuracy rate by using canny edge detector and Hough transform. Automatic segmentation is achieved through the use of the canny edge detector and Hough transform for localizing the iris and pupil regions. The energycompaction characteristics of DCT _DHT [10,15] are used to capture iris texture variations contained in both horizontal and vertical detail sub bands of second level wavelet decomposition. In order to reduce the size of the database, binary bit stream instead of binary image is stored in the database for matching purpose. The same is repeated for finger print [5]in which segmentation is achieved by sobel edge detection and feature extraction is performed using DCT_DHT. The reduced feature vector size provides faster recognition rate. Biometric systems[6] that perform the identification of person based on the information obtained from multiple biometric traits are known as multimodal biometric system. Although there has been much research on combining different biometrics for variety of purposes. In this paper it is focused on the combination of fingerprint[4,9] and iris, which are two of the characteristics that can reach the best recognition performance for high security application and the security is provided for the database through visual cryptography. The main application of this multimodal biometric database is for ATM banking to increase the security of the system, visual cryptography is done by the process such that the bit stream obtained is first encrypted using the user key obtained from user password and then the encrypted bit pattern template is stored. The use of multimodal biometrics for key generation provides better security, as it is made difficult for an intruder to spool multiple biometric traits simultaneously. Experimental results show that the proposed algorithm provides better matching along with less computational complexity and better security on a thorough experimental investigations based on two types of biometrics (i.e. fingerprint and iris), into the effectiveness of various fusion approaches in both unimodal and multimodal biometrics and the application of relative quality-based score level fusion to reduce the effects of relative degradation in
multimodal fusion.. The scope of the investigation includes the use of verification scores obtained from different types of feature extracted from biometric data for ATM banking. their usage. And Table 1, shows the timing summary for the user module. Visual Cryptography Key Encrypted Iris image System Data Base Decrypted Iris Fusion Of IFT & FPFT Yes New Finger Print Pre- Processing Feature Extraction Using DCT+DHT FPFT Matching No New Eye Segmentation Normalization Feature Extraction Using DCT+DHT IFT Figure 3: User Authentication for ATM banking IV. EXPERIMENTAL RESULTS Multimodal biometric systems take input from single or multiple sensors measuring two or more different modalities of biometric characteristics. Larger data base with diversified populations should be used for evaluating the iris recognition algorithms. Most researchers use the publicly available iris database for the experiments, here we have taken CASIA data base[16] and University of Bath database[17].these data bases have limited number of iris images and the images are captured under controlled condition. The inputs of the project are images of iris and finger print of 8x8(8 bit width) matrix for both the images. Then the 8x8 input bit of iris image goes to the canny-edge detector and the output will be 16- bit. The 8x8 input bit of fingerprint goes to the sobel detector and the output will 8-bit. The output canny edge detector and sobel detector will go to the feature extraction process of DCT[10] and the output will be 16 bit for iris image and 8 bit finger print image. The output of the DCT will go to the DHT[10] for further compression of the images and output of IRIS will be 20bit and for the finger print image is 10bit. The output of the both DHT will be go to the fusion process and both the outputs will be combined and output is of 30bit The Output of the fusion will encrypted using visual cryptography process and the output will be stored in the Memory. Table 2, represents the synthesis report of different macro statistics and Figure 5: Simulation results and RTL view of the authentication module. Table 1.Timing Summary Time Minimum period Minimum input arrival time before clock Maximum output required time after clock Maximum combinational path delay Duration 0.946ns 6.116ns 0.562ns No-path found Table 2: Synthesis report Macro Statistics Used Adders/Subtractions 142 Registers 232 Multiplexers 41 Logic shifters 1 Xors 25 These data bases have limited number of iris images and the images are captured under controlled condition. The inputs of the project are images iris and finger print of 8x8 (8 bit width) matrix for both the images. The 8x8 input bit of iris image goes to the canny-edge detector and the output will be 16-bit. The 8x8 input bit of fingerprint goes to the sobel detector and the output will be of 8-bit. The output canny edge detector and sobel detector will go to the feature extraction process of DCT and the output will be
16 bit for iris image and 8bit finger print image. The output of the DCT will go to the DHT for further compression of the images and output of Iris will be 20bit and finger print is of 10bit. The output of the both DHT will be go to the fusion process and both the outputs, will be combined and output is of 30bit The output of the memory will be decrypted and compare with the fusion output and gives the result of the matching module. Figure 5 shows the RTL view of the user authentication module and also its simulation results. Table 1 represents the synthesis report of different macro statistics and their usage. And Table 2, shows the timing summary for the user authentication module. V.CONCLUSION In this paper firstly, in designing a multimodal biometric system, we have presented a brief overview and challenges involved by the selection of modalities which strongly depends on the application and level of security[7,8] involved and this will also decides the complexity in designing a system. Secondly, the level of fusion (like sensor, feature, match and decision) plays a crucial role in making a decision. The sensor and feature level fusion preserves a rich set of information and they also result in a high computation, and hence its preferred to increase security. Thus, choosing the level of fusion is a challenging issue and further depends upon type of sources employed, application and the level of security. Thus the main aim in designing a multimodal biometric system is to address the drawbacks in designing a unimodal biometrics based in increasing the security system such as non universality, less sensitive to spoof attacks and noise REFERENCES [1] S.S, Das and J. Debbarma, Designing a Biometric Strategy (Fingerprint) Measure for Enhancing ATM Security in Indian e-banking System, International Journal of Information and Communication Technology Research, vol.1, no. 5, pp.197-203, 2011. [2] W.W.N. Wan, C.L. Luk, and C.W.C. Chow, Customers Adoption of Banking Channels in Hong Kong, International Journal of Bank Marketing, vol. 23, no. 3, pp. 255-272, 2005. [3] Wikipedia the free encyclopedia, Biometrics, Downloaded March 20, 2012 from http://en.wikipedia.org/wiki/biometrics. [4] L. O Gorman, Overview of fingerprint verification technologies, Elsevier Information Security Technical Report, vol. 3, no. 1, 1998. [5] G.B. Iwasokun, O.C. Akinyokun, B.K. Alese, and O. Olabode. Fingerprint enhancement: Segmentation to thinning, International Journal of Advanced Computer Science and Applications, vol. 3, no. 1, pp. 15-24, 2012. [6] C. Roberts, Biometrics. Downloaded February 13,2012,http://www.ccip.govt.nz/newsroom/informoat ionnotes/2005/biometrics.pdf, [7] C. Michael and E. Imwinkelried, Defence practice tips, a cautionary note about fingerprint analysis and reliance on digital technology, Public Defense Backup Centre Report, 2006 [8] M. J. Palmiotto, Criminal Investigation, Chicago: Nelson Hall, 1994 [9] D. Salter, Fingerprint: An Emerging Technology, Engineering Technology, New Mexico State University, 2006 [10]P.John Paul, P.N.Girija, A High Performance Novel Compression Technique using Hybrid Transform for Multimedia Applications IJCSNS International Journal of Computer Science and Network Security, VOL.11 No.4, April 2011.pp119-125 [11]Wildes, R.P, Iris Recogntion: An Emerging Biometric Technology, Proceedings of the IEEE, VOL. 85, NO. 9, September 1997, pp. 1348-1363 [12]Moni Naor and Adi Shamir. Visual Cryptography, EUROCRYPT 1994, ppl- 12. [13]Shamir, Adi. How to share a secret. Communications of the ACM 22 (II): 1979,6 12-613. [14]P.S.Revenkar, Anisa Anjum, W.Z.Gandhare. Survey of Visual Cryptography Schemes ". International Journal of Securityand Its Applications,Vol. 4, No. 2, April, 2010. [15]Peng Chungan, Cao Xixin, Yu Dunshan, Zhang Xing. " A 250 MHz optimized distributed architecture of 2 D 8X8 DCT, 7 th International Conference on ASIC, pp.189-192,oct.2007. [16] CASIA Iris Database, http://www.sinobiomerics.com/databases.htm,2007. [17] University of Bath Iris Database, http://www.bath.ac.uk/eleceng/research/sipg/irisweb/ database.htm,2007.