A secure architecture for m commerce users using biometerics and pin distribution technique-54830-rewrite journal.

International Journal of Technical Innovation in Morden
Engineering & Science (IJTIMES)
Impact Factor: 3.45 (SJIF-2015), e-ISSN: 2455-2584
Volume 2, Issue 4, April-2016
IJTIMES-2015@All rights reserved 12
A SECURE ARCHITECTURE FOR M-COMMERCE USERS USING
BIOMETERICS AND PIN DISTRIBUTION TECHNIQUE
1
P.Angelinsundari , 2
Mr.Rajasekaran
1
Post Graduate Student, 2
Assistant Professor ,Computer Science and Engineering, SRM University, Chennai, India
Abstract - M-commerce stands for mobile commerce are designed to provide value added services for mobile
customers and also to generate market opportunities. Security is still a challenge when it comes to m-commerce. In
this paper a biometric based authentication model along with secure PIN is being proposed. Biometric along with
mobile device identify is being proposed in this paper. This concept is called as M-identity.
M-identity authentication allows only the users whose biometric information is stored in the mobile device. In this
paper, fingerprint biometric is been demonstrated as an example to show its application on secure m-commerce
application.
Introduction
Mobile commerce has seen tremendous development in last five years. The Bank of America states that around US$67.1
billion will be the purchase through mobile devices by the European and US shoppers. There are various factors to drive
the m-commerce, but the main among all is the consumer demand for buying and selling goods and services. Online
banking and bill payments are also the major attraction for m-commerce. All the banks and brokerages are offering
mobile apps for their customers for online banking and trading. The final factor to be considered for the growth of m-
commerce is the rapid adoption of online commerce due to stronger security practices. The authentication processes use
multiple factors and layers of security levels. This has motivated a variety of m-commerce services and products into the
industry. Some of the newly immerged services are the mobile ticketing, mobile ATM, mobile money transfer, purchase
and delivery of contents like video and audio and location based services. New applications are also finding its way for
m-commerce. Mobile payments are made much easier now with the help of smart phones. This can provide recurring
customers and revenue for the developers.
Fig 5.1 SYSTEM ARCHITECTURE DIAGRAM
M-commerce can be simply defined as the deliverance of electronic commerce in convenience of the user through
wireless technology. M-commerce has broken the traditional purchase habit which was through e-commerce from home
or working place. M-commerce can be accessed anytime anywhere. All it needs is the internet connectivity. Other factors
like electricity, video confenerncing is no more a barrier in m-commerce. Development of m-commerce and its related
technologies paved the way for LBS- location based services, mobile shopping, mobile financial services, mobile
ticketing, mobile marketing and mobile entertainment. Multitasking is very much achievable in m-commerce.
M-commerce users are those who expect immediate response. The technology exists through wireless devices like PDA,
PC's, and cell phones. It facilitates user to purchase goods anytime anywhere and on the go. The main drawback in the m-
commerce is that the external factors can easily hack the authentication PIN using third party tools. There is no
authentication of user and merchant concept in m-commerce and hence security is not assured completely. Biometric

International Journal of Technical Innovation in Morden Engineering & Science (IJTIMES)
Volume 2, Issue 4, April-2016, e-ISSN: 2455-2584, Impact Factor: 3.45 (SJIF-2015)
feature extraction algorithm which is already in existence failed to cover this aspect of security. The lack of effective user
authentication and lack of security for m-commerce applications are the major threat for m-commerce.
In this paper we have proposed the user and merchant authentication and PIN distribution concepts. Biometric server is
being used for IRIS and face feature extraction for each customer of online mobile shopping. K-NN algorithm for IRIS
recognition and Gabor filter for face recognition are being used here along with the user authentication PIN distribution.
Effective encryption algorithms among RC4 are along being implemented in this proposed model.
Related Work
In Statistical Approach for Iris Recognition Using KNN-Classifier
Iris recognition is the most wanted research topic around the world. Due to its unique features, Iris recognition of a
person is very popular. There are several number of methods been proposed for iris recognition in last couple of years. In
this research paper we have proposed a statistical texture feature based iris method along with KNN classifier. Statistical
measure comprises of standard deviation, mean, skewness, entropy, etc. KNN classifiers use the Euclidean distance
concept to match the input iris image with the trained iris image. This concept provides a good classification accuracy
with reduce FAR/FRR. Iris recognition uses high resolution image of the individual’s eyes. The reason for choosing the
iris is because the human iris has a lot of detailed and minute characteristics like coronas, stripes, freckles, etc.
These are unique characterisitcs of the iris and differ from person to person. In the field of biometric there are other
features which hold 13 to 60 distinct characteristics but the iris has 266 unique spots. Eye is something which is unique
and remains stable over the period of time. The whole iris recognition system likes on the iris recognition algorithm and
image acquisition. So this proposed method concentrates on the extraction of the iris features and has considered the
above mentioned factors as well.
Though there are many proposed methods based on the iris concept, the algorithm proposed by Daugman Wildes et al
bles and Boashash are the world known method so far. Daugman's algorithm comprises of multi scale Gabor filter for
coarse quantization of the local texture signal and iris code at 2048 bits. The gray level co occurrence matrix (GLCM) is
being used for texture classification. GLCM is calculated from the original texture image and it is compared with the
input texture image to calculate the difference along the non singleton dimension.
In Business- to-Consumer Mobile Agent-Based Internet Commerce System (MAGICS)
In this paper we have proposed MAGICS, which is mobile agent based system for business to consumer concept in e-
commerce or m-commerce applications. In this system, the consumers buying requirements are sent to the agent/proxy
server via web browser or through WAP (wireless application protocol) terminal. Now the mobile agent’s carries out the
task like contacting the merchant to fetch details on the offers, and to complete the purchases. In mobile commerce, the
consumers can do a research and evaluation in the digital market space before doing a purchase in the physical market.
There is a mathematical model being proposed to evaluate various decision factors. We have also built a prototype to
evaluate the basic function of MAGICS (mobile agent based internet commerce system). We have also introduced a
analytical model to minimize the cost of a product by deciding on how many agents should be sent to compare the prices.
Real price information and four price distributions are analyzed in this model. The output of this analysis is used to
design the mobile agent based shopping applications for m-commerce and for e-commerce. As we all know that in
physical shopping the consumer needs to physically visit several hops to compare and evaluate the prices using some
simple methods.
Though some of the users like the traditional way of shopping it is time and energy consuming. After choosing the
products the buyer purchases in the selected physical shop. There are some draw backs in the traditional shopping mode
as it is limited to a small geography. Say for example the consumer may visit shops in an around a small locality. In such
cases the consumer may not know whether the best price is found within the lowest range for the items. The current way
of shopping through the web has broken the geographical limitations and providing more convenience to the consumer.
In Fuzzy Logic in Biometrics
Fuzzy logic is an easy concept to understand. The mathematical concept behind this concept is also pretty simple and
straight forward. Fuzzy logic is like an intuitive approach and is flexible. Fuzzy logic can easily fit into any given system
without disturbing the existing system. It can be inserted without starting again from the scratch. Fuzzy logic handles the
imprecise data. It is highly impossible to inspect everything in a closer angel. Fuzzy reasoning makes this easier by
creating a process with the understandings. It can model non linear functions as well. It cannot be considered as a
solution but it is a convenient solution for non linear problems. It is nothing but the codification of common sense.
Common sense gets you to the right decision. There are many controllers who perform great with the fuzzy logic. The
main constrain with the fuzzy logic is that it consumes tremendous amount of time in the process of matching and hard to
develop a model from fuzzy system.
Face Recognition
Facial recognition is the most commonly used biometric system to identify or verify a person with the digital image or
from a video source. There are many ways to do the recognition but one such way is to compare the facial features of the
image with the facial database. Some of the facial recognition algorithms use the feature extraction concept and analyze
the relative position, size or shape of the features like nose, eyes, cheekbones, jaws. After fetching the information’s it

searches the database for the matching features. There are other algorithms which compress the original image and save
only the important features of the face in the database. A probe image is used to compare with the face data. The most
successful system is the template matching techniques applied on salient facial features.
The recognition algorithm comprises of geometric and photometric. Geometric which considers the distinguishing
features and photometric is a statistical approach which derives values from the image. Some of the popular recognition
algorithms include Eigen faces with Principal component Analysis, Elastic Bunch Graph matching using fisher face
algorithm, hidden Markov model, linear discriminate analysis, multi linear subspace learning using the tensor
representation and the neuronal motivated dynamic link matching.
Face recognition involves the physical characteristics or behavioural characterises of a person. Image processing starts
with an input image which can be a photograph or frames or videos for which the output can be either be set of
characteristics or an image. The most common image processing is the two dimension signal of the image and applying
standard signal-processing techniques to it. Face detection is viewing the human face in a digitalised form. It considers
the important unique features of the face and ignores the rest like the building, tree, bodies in the photograph. The
provided face is compared with the already saved image in the database. If the image matches with the image in the
database then the person is authenticated and can move further with the process.
IRIS
Identifying a person with the iris is the most popular recognition method. Though there are a number of methods for iris
recognition we have proposed a statistical method with feature based iris matching method using the KNN classifier.
Statistical measure includes entropy, standard deviation, skewness, etc. and six features are computed.
PIN Distribution
The secure PIN distribution is encrypted using RC4 algorithm and then sent for authentication to the external server. In
these server important details of the user namely timestamp, pin distribution, user id and user IP address are obtained.
This architecture has high secure pin distribution by splitting the 4 digit pin in two parts and 2 digits each and forming a
sequence. After which each part is processed to produce a decimal sequence. The pin distribution database query
management is where the new data comes. The administrator should analyze whether the users intention is only to hit the
database and initiate the transaction. Let’s see how this concept works with an example. Consider a pin number 1786. It
randomly generates 4 digit numbers for odd/even remainder. Let’s randomly consider odd remainder 1643 and 2541 for
even remainders... The pin number will be now converted with the base 10. I.e. 1786/10. The remainder will be 2. Now

let’s take a 4 digit number 2541 for the sequence. Taking into consideration both the pin and digit number 2541 and 1786
the final sequence is framed by mapping the values using the sequence table.
Technique and Algorithm
4.1 Algorithm
Pre Processing
In the pre processing module, we take the face image as input. The input image will be resized to the system required
size. After which the RGB image is converted into a gray image. In the gray scale image the unwanted details are
removed using the Gaussian filter.
Gabor Feature Extraction
The four Gabor feature images provides a valuable set of discriminate and rich texture patterns for ordinal feature
representation. Now different methods of ordinal feature extraction are applied on these four Gabor feature images. The
ordinal measures that are being taken from the Gabor like the phase, magnitude, real and imaginary parts are named as
GOM-m, GOM-r, GOM-i, and GOM-p respectively. We also use the four tri-lobe and four di-lobe ordinal filters to
extract the robust ordinal features in different directions with orientation values 0, 45 90 and 135 degree.
Fig 4.1 GABOR FEATURE EXTRACTION
LDA (Linear Discriminant Analysis):
After the Gabor features being extracted there is redundancy and 5760 dimensional GOM histogram is over complete for
visual representation. Hence it is very necessary to reduce the dimensions of feature vector so that we derive a compact
representation of the facial image. We have used Linear Discriminant Analysis (LDA) in this method. LDA is a widely
used technique on face recognition to reduce the dimensions and classifications. LDA selects a set of projection vectors
maximizing the between class scatter matrix (sb) and minimizing the within class scatter matrix (Sw) in the projected
feature space.
The Segmentation Process involves the following Steps.
1. The eyelashes are reduced from the original image using median filtering.
2. Hough Transform is applied to analyze the pupil and iris circles.
3. A mask is created for the iris boundary.
4. Mask is created for pupil region.
5. The convolving mask and image is achieved in step 4.
The above steps helps to find the different parameters of pupil and iris region like the radius of the iris, centre of the iris
as well as the pupil. Now the iris region is separated from the eye part. Now the iris region has to be transformed so that
it has fixed dimensions to allow comparisons. The inconsistency in the dimension of the iris is due to the pupil dilation
from varying levels of illumination.
When it comes to pattern recognition filed, KNN is the most important non-parameter algorithms and it is the supervised
learning algorithm. The classification rules are generated from the initial image without any additional data. The KNN
classification works on the training samples to choose the nearest neighbours to the test samples. This is then judged with
the category which has the largest category probability.

Fig 4.2 SEGMENTATION PROCESS FOR
IRIS
RC4
RC4 was initially designed for RSA security by Ron Rivest in 1987. It is a cipher with variable key size stream and byte-
oriented operations. The algorithm works with random permutation. The experiments show that the cipher is likely to be
greater than 10100 [ROBS95]. It is being computed that 8 to 16 machine operations are required per output byte, and the
cipher can run very quickly in software. RSA security kept RC4 as a trade secret. In 1994, the RC4 algorithm was posted
in the internet. RC4 algorithm is simple and quiet easy to explain. A variable length key which ranges from 1 to 256
bytes (8 to 2048 bits) is used for initialisation of 256 byte state vector S, with the elements S[0], S[1],....S[255]. S
contains a permutation of all 8-bit numbers from 0 through 255. A byte k from S is selected for encryption and
decryption by selecting one of the 255 entries in a systematic fashion. When a value k is generated then the entries in S
are again permuted.
The key scheduling algorithm is used for permutation array. The initial step of the algorithm is to initialise the S table
with identity permutation. The values which are inside the array are equal to their index. After the S array is being
formed now it’s being shuffled to make it a permutation array. This is simply done by iterating 256 times the following
actions after initializing i and j to 0.
 Calculate j=j + S[i] + key [i modkeylength]
 Exchange S[i] and S[j]
 Increment i
Once i reach 256 iterations, the S array is completely initialized.
Here is the pseudo code for key scheduling algorithm
For i from 0 to 255, S[i] = i end for j=0
For i from 0 to 255, j= (j+S[i] + key[i mod key length]) mod 256 swap values of S[i] and S[j] end for.
Now the generated array S is used for the RC4 algorithm to generate keystream.
Conclusion
In spite of so many limitations in mobile device, user authentication scheme has delivered immense security. The
merchant’s authentication takes care that the user is dealing with the right person for transaction. The ordinal measures
on Gabor (GOM) magnitude, real and imaginary responses, phase and scales are extracted. The ordinal information’s are
encoded in to GOM maps Gabor Ordinal Measures provides a promising solution for face image analysis. IRIS algorithm
is implemented along with the KNN algorithm. Hence this proposed solution is a unique solution for m commerce
transactions.
References
1. John Daugman, ―How Iris Recognition Works‖ Invited paper IEEE transactions on circuits and systems for video
technology, vol. 14, no. 1, January 2004.

2. John Daugman, ―New Methods in Iris Recognition‖ IEEE transactions on systems, man, and cybernetics— part b:
cybernetics, vol. 37, no. 5, October 2007.
3. Pillai, Jaishanker K.; Puertas, Maria ; Chellappa, Rama ―Cross-Sensor Iris Recognition through Kernel Learning ‖
Pattern Analysis and Machine Intelligence, IEEE Transactions on Volume: 36, Issue: 1, Publication Year: 2014 ,
Page(s): 73 – 85.
4. Chang-lung Tsai ,chun-jung chen, Deng-jie Zhuang, Secure OTP and Biometric Verification Scheme for Mobile
Banking,2012 IEEE.
5. Mangala Belkhede,venna Gulhane,Dr.preeti Bajaj, Biometrics mechanism for enhanced security of online
transaction on android system, feb ICACT 2012.
6. DR.B.Vanathi, Shanmugam, Enhancing secure transaction and user authentication method based on mixed
fingerprint mechanism using fuzzy logic in m-commerce, MAY – 2014.
7. S.Sanderson.J.Erbetta,‖ Authentication for secure architecturebased on iris scanning technology ― IEE colloquition
on visual biometrics, 2000.

A secure architecture for m commerce users using biometerics and pin distribution technique-54830-rewrite journal.

Recommended

Recommended

More Related Content

What's hot

What's hot (20)

Similar to A secure architecture for m commerce users using biometerics and pin distribution technique-54830-rewrite journal.

Similar to A secure architecture for m commerce users using biometerics and pin distribution technique-54830-rewrite journal. (20)

Recently uploaded

Recently uploaded (20)

A secure architecture for m commerce users using biometerics and pin distribution technique-54830-rewrite journal.