Implementing Iris in the Railway Control Office Application for Secure Saas in Cloud Environment

Dr. K. Meena et al. Int. Journal of Engineering Research and Applications www.ijera.com
ISSN: 2248-9622, Vol. 5, Issue 12, (Part - 4) December 2015, pp.42-47
www.ijera.com 42|P a g e
Implementing Iris in the Railway Control Office Application for
Secure Saas in Cloud Environment
Dr. K. Meena*, Dr. M. Manimekalai**, R. Raghuraman***
*Former Vice-Chancellor, Bharathidasan University, Trichy, Tamilnadu, India
** Director and Head, Department of Computer Applications, Shrimati Indira Gandhi College, Trichy, India
***Principal, Zonal Railway Training Institute, Southern Railway, Trichy, Tamilnadu, India.
ABSTRACT
Technology plays a vital role in each and every part of the world. In particular ―Cloud‖ computing - a
moderately recent term, characterizes the path to develop the advancement in the world of computer science.
Further, Cloud provides an affordable environment for its users through different forms of services such as SaaS
(Software as a service), PaaS (Platform as a service), and IaaS (Infrastructure as a Service). Cloud computing is
also an Internet-based computing where a large pool of systems are connected in private or public networks, and
provide dynamically scalable infrastructure for application data as well as file storage. Security of Cloud
computing is an evolving sub-domain of network security, computer security and information security. In spite
of its advantages, Cloud environment has many security flaws such as loss of important data, data leakage and
something related to cloning, resource pooling etc. Security of Cloud Computing is an emerging area for study.
It includes several security and privacy issues with challenges and solutions for many security issues of cloud
computing. The Control Office Application (COA) is the latest addition to train operations related IT application
of Indian Railways. Along with the Freight Operations Information System (FOIS), COA has led to a complete
transformation in train operations and facilitates all information on train operations being computer generated. It
is this application that feeds the National Train Enquiry System (NTES) which provides passengers with up to
date information on train running. COA also provides train operations information to FOIS and ICMS. The
objective of the Indian Railways is to further improve the operations by using technological aids that enable
quicker data capture and intelligent applications that provide better planning and forecasting tools. To overcome
these issues, in Cloud computing, we can use SaaS (software as a service). In this paper, we have proposed a
new IRIS algorithm to authenticate the users of COA software in the cloud environment.
Keywords – COA, SaaS, PaaS, IRIS, Authentication
I. INTRODUCTION
In the present era, Cloud computing has become
one of the most hyped IT innovation [1]. In the world
of modern technology, Cloud computing technology
is an innovative concept, which affords great
prospects in many domain areas. Cloud computing is
a combination of computers and servers that are
openly accessible via internet [2]. Cloud computing
allows consumers and business people to deploy
applications without installation and to access their
personal files from any computer with internet
facility. Cloud computing endows the mixture of
internet based on stipulated services like software,
hardware, server, infrastructure and data storage [3].
Now a days, cloud computing has become more
popular in the field of technology. To authenticate the
official users in cloud computing using IRIS
recognition system, we have made assessment of
some of the existing authentication formats. Firstly,
in cloud computing the conventional username and
textual password is used. But this method is
considered too simple to hack. Some systems have
projected graphical and 3D password but it entails
more space and time consuming process on
validation.
According to a Gartner Group estimate, SaaS
sales in 2010 reached $10 billion, and projected to
increase to $12.1 bn in 2011, up by 20.7% from
2010. The Customer Relationship Management
(CRM) continues to be the leading market for SaaS
[4]. In the earlier published paper titled ―A Privacy
Preserving Repository for Securing Data across the
Cloud‖, the researchers projected the privacy
preserving storage area for acceptance of
incorporation of the requirements from clients to
share data in the cloud and maintain their privacy,
collect and amalgamate the apt data from data
sharing services, and return the integration results to
users [5].
The universal approach is to deliver the data
concept with information and control over data
privacy is the stipulation under privacy policies
explicit to the data shared [6]. SLA negotiation is
also a matter of previous research based in the Grid
community [7] and presently widening into the cloud
RESEARCH ARTICLE OPEN ACCESS

[8,9]. Within the cloud, the SLA negotiation process
grips the creation of a SLA captured by deploying the
WS agreement XML standard [10]. In the cloud, the
service provider confers SLA on behalf of the user
with cloud infrastructure providers. In addition, the
process of an assessment of privacy has rebuffed into
a separate area of research in the form of Privacy
Impact Assessments (PIA) [11]. In 1920‘s, PIA have
emerged from existing work on data access by
Organizations to more specific mentions of PIA in
terms of technology in the 1970s, wherein a wide
range of research has been undertaken [12].
As a result of widespread fragmentation in the
SaaS provider space, there is a promising trend
towards the advancement of SaaS Integration
Platforms (SIP). These SIPs permit the subscribers to
employ the multiple SaaS applications through a
common platform. In addition, they also offer new-
fangled application developers an opportunity in
quickly developing the new applications. According
to a survey of 600 enterprises by Enterprise Strategy
Group 2012 it is stated that the use of SaaS is bound
to consistent rise. In terms of the result of the present
survey, it is established that 46% of existing users
have adopted SaaS, 17% do not use but are planning
to use, 21% don not use or plan but were interested to
use, 14% do not use, plan or show interest and 1%
were not clear [13]. However, the safety is one of the
most significant concerns of SaaS. In a survey 51%
of the people attributed security as the primary
concern where as 40% opined incorporation with
other application, 34% lack of customization and
33% total cost of ownership as other possible reasons
for not using Saas[14].
II. SECURITY ISSUES OF SAAS IN
CLOUD COMPUTING
In Software as a Service (SaaS) model, the customer
desires to be reliant on the service provider for proper
security measures of the system. The service provider
must guarantee that their multiple users don‗t get to
see each other‗s private data. Subsequently, it
becomes more important for the users to make sure
that right security measures are in place and also it is
not tricky to get an assurance that the application will
be available, as and when needed by them [15].
Cloud computing providers have a definite role to
solve the common security challenges that a
conventional communication systems faces under
different situations. At the same time, they should
also provide solutions with other issues inherently
catered to by the cloud computing paradigm itself.
2.1 Authentication and Authorization
The authorization and authentication applications
employed in enterprise backgrounds need to be
distorted, so that they can work even under a safe
cloud environment. Forensic tasks will become more
difficult because it will be very hard or may not be
possible for investigators to access the system
hardware physically.
2.2 Data Confidentiality
Confidentiality may relate to the hindrance of
unintentional or intentional unauthorized disclosure
or distribution of secured private information.
Confidentiality is strongly related to the areas of
encryption, intellectual property rights, traffic
analysis, covert channels, and inference in cloud
system. Whenever a business, an individual, a
government agency, or any other entity wants to
distribute the information over cloud, confidentiality
or privacy is a big question which needs to be
addressed.
2.3 Availability
The availability guarantees reliable and timely access
to cloud data or cloud computing resources to the
authorized users. The availability is one of the
immense concerns of cloud service providers, as
cloud service if disrupted or compromised in any way
may affect a large no. of customers than in the
conventional model.
2.4 Information Security
In the SaaS model, the data of an enterprise is piled
up outside the enterprise boundary, which is
technically at the SaaS vendor premises.
Consequently, the SaaS vendor needs to implement
additional security features to ensure data security
and thwart breaches due to security vulnerabilities in
the application or by malicious employees. This will
help us to understand the need for use of strong
encryption techniques for data security and highly
competent authorization to control access over
private data.
2.5 Data Access
Data access issue is primarily associated with
security policies afforded to the users while accessing
the data. Each and Every Organization have their
own security policies based on which their employees
can have access to a particular set of data. These
security policies must be held on by the cloud to
avoid intrusion of data by unauthorized users. The
SaaS model must be stretchy enough to integrate the
specific policies put forward by the organization.
2.6 Network Security
In a SaaS development model, highly sensitive
information is obtained from the various enterprises,
then processed by the SaaS application and stored at
the SaaS vendor‗s premises. All data flow over the

network has to be secure to facilitate prevention of
leakage of sensitive information.
2.7 Data Breaches
Since data from different users and business
organizations reside together in a cloud environment,
breaching into this environment will potentially make
the data of all the users vulnerable. Thus, the cloud
serves as a high potential target.
2.8 Identify Management and Sign-on Process
Identity management (IDM) or ID management is the
field that enables to identify the individuals in a
system and controlling the access to the resources in
that system by placing restrictions on the established
identities. The area of IdM is considered as one of the
biggest challenges in information security. When a
SaaS provider intends to manage the users for access
control within the enterprise, it becomes a really
mundane task.
III. AUTHENTICATION IN CLOUD
As cloud users store their information
through an assortment of services across the Internet,
it can be easily accessed by unauthorized people [16].
So security is the most important issue in cloud
computing. To provide security, it necessitates proper
authentication techniques in cloud computing.
Typically, authentication is done based on
information about one or more of the following: (i)
Knowledge of the subject, such as password or secret
information. (ii) Possession of the user, such as smart
card, passport, driver‘s licence, etc. (iii) Biometric
traits of the client, such as fingerprint, voice, iris, etc.
[17].
The data leakage and security attacks can
originate mainly due to insufficient authentication
[18]. Cloud services are paid services as a result of
which identity of the authorized user becomes a
major concern. In this research paper, we focus on
the safety measures in cloud computing, more
particularly on authentication. To solve
authentication problems in cloud computing, there
are various traditional as well as biometric techniques
as highlighted below which do have some negative
aspects as well.
3.1 Traditional Authentication Scheme
1) Password – A login id and a password
combination is the most commonly used method of
authentication but not well secured [19]. It is very
easy to hack the password by various tools. 2) OTP
– OTP is a One Time Password wherein password is
provided upon request. An OTP can thwart a
password from being stolen and reused [20]. This
password is valid for a limited period of time (say 5
minutes) and can be used only once. This kind of an
authentication is fairly expensive.
3.2 Biometrics Authentication Techniques
1) However, Now-a-days, Biometrics is one of the
most extensively used security system. It helps
to overcome a lot of drawbacks of above stated
techniques of authentication. Biometrics can be
defined as an automated methodology in
exceptionally identifying the humans by their
behavioural or physiological characteristics
[21].
2) There are several biometric techniques as listed
below:
3) Voice Recognition – As the name suggests
voice recognition involves authentication with
respect to vocal data. Voice recognition is used
to authenticate user‘s identity based on patterns
of voice pitch and speech style. But a user‘s
voice can be easily recorded and may be used
by an unauthorized user. Also voice of a user
may change due to sickness, which makes
identification difficult.
4) Signature Recognition – Signature recognition
is used to authenticate user‘s identity based on
the traits of their unique signature. People may
not always sign in a consistent manner and
hence verifying an authorized user is difficult.
5) Retinal Recognition – Retinal recognition is for
identifying people by the pattern of blood
vessels on the retina. But this technique is very
intrusive and expensive.
6) Iris Recognition – Iris recognition is a method
of identifying people based on unique patterns
within the ring-shaped region surrounding the
pupil of the eye. As in the case of retina, this
technique is also intrusive and expensive.
7) Fingerprint Recognition – Fingerprint
recognition refers to the automated method of
verifying a match between two human
fingerprints. The dryness of fingers, soiled
fingers etc. can affect the system and may show
an error.
8) Hand Geometry Recognition – Hand Geometry
biometrics is based on the geometric shape of
the hand. It includes the size of the palm, length
and width of the fingers etc. But this technique
has some drawbacks like it is not ideal for
children as with increasing age their hand
geometry tends to change and also constant use
of jewellery may result in change in hand
geometry. This technique is not valid for
persons suffering from arthritis, since they
would not be able to put the hand on the
scanner properly.
9) Palm recognition – Palm recognition is based
on ridges, principal lines and wrinkles on the
surface of the palm. This technique is very
expensive and not appropriate for children as

their lines of palm change drastically once they
are fully grown up.
IV. IMPLEMENTING PROPOSED IRIS
ALGORITHM IN CONTROL OFFICE
APPLICATION
In the proposed algorithm, the following steps will
take place: Pre-processing, Feature Extraction and
Feature Classification.
4.1 Pre-Processing
In this first step, the following processes would take
place. Gray scale conversion, Median Filtering, Pupil
Center Detection, Canny Edge Detection, IRIS
Radius Detection, IRIS Localization, IRIS Unrolling.
4.1.1 Gray Scale Conversion
In this step, the given image with the kernel radius ‗r‘
and size ‗m x n‘ is converted into image of size ‗m x
n‘.
Input: Image X of size m n, kernel radius r.
Output: Image Y of size m x n.
Step 1: Read the image.
Step 2: Convert the image into m x n matrix of pixels
Step 3: For each pixels in both row and column,
apply gray scale conversion formula of dividing the
RGB value of each pixel by 3.
4.1.2 Median Filtering
Input: Image X of size m n, kernel radius r.
Step 1: Initialize each column histogram h0; : : : ; hn1
as if centred on row 1.
Step 2: for i = 1 to m do
Step 3: Shift the rst r column histograms h0; : : : ; hr1
down 1 pixel.
Step 4: Combine these r column histograms to form
the kernel histogram H.
Step 5: for j = 1 to n do
Step 6: Set pixel Yi;j equal to the median of H.
Step 7: Remove column histogram hjr1.
Step 8: Add column histogram hj+r.
Step 9: end for
Step 10: end for
4.1.3 Pupil Center Detection
Input: Image X of size m x n.
Step 1: Scan through the median image from top left
to bottom right and make no assumptions about the
position of the pupil (or eye for that matter).
Step 2: The algorithm begins by finding a pixel that
is below the threshold (a combination of the lowest
intensity in the current image and some variance)
Step 3: Find the amount of pixels adjacent to the right
that has intensity below the threshold as well.
Step 4: This amount is called the block size. The
centre of the block is the suspected centre of the
pupil.
Step 5: If this block is the largest observed so far, it
determines if a block of pixels going in the vertical
direction up and down from the center of the block
(Effectively making a cross) are also below the
threshold and have some variance.
Step 6: If so, the maximum block size is updated and
the centre to return is reset to new centre.
4.1.4 Canny Edge Detection
Using the canny edge detection,
Step 1: Detects the edges of the image based on the
current threshold and sigma values.
Step 2: The canny edge detector will generate a
binary image (black and white) that shows the edges
of the image.
Step 3: This image is saved into the subject as the
edge image.
4.1.5 Pupil/ IRIS Radius Detection
If the percentage of the pixels along the circle defined
by the current radius and the pupil centre that are
black is greater than the given threshold percentage,
the iris has been found.
4.1.6 IRIS Localization
1) The radius identified by the pupil center
detection and finds a radius for which the
circle in the edge image has at least a certain
amount of black pixels (edges) on or nearly
on the circle.
2) If the proportion of the iris radius meeting
this criterion is between two bounds then the
iris radius is successfully found.
4.1.7 IRIS Unrolling
1) Unrolls the iris of the subject, defined as the
area between the pupil radius and the iris
radius.
2) The iris, a circular object is transformed
universally into a rectangular image that
counteracts the distortion of the wrapping of
the iris around the eye.
4.2 Feature Extraction (Extracting a 8*12 Iris
Pattern from Edge Detected IRIS Image)
1. Take the 8-Bit BMP Image produced from
previous Algorithm as Input and open this BMP file
in binary Read Mode.
2. Read the raster Data and Store the raster Data into
a Matrix of vector size. Where vector Size = file size
- (54+(4*256)).
3. Then a 8*12 Iris Pattern is extracted from Edge
Detected BMP using following logic-
for (x=0;x<=originalImage.rows-1;x++) {

for (y=0;y<=originalImage.cols-1;y++) {
if ( y < 30 && x = (original
Image.rows/2)+4) && Gray Value = =255) {
for (i=0;i<8;i++){
for (j=0;j<12;j++){
*(edgeImage.data + (i * edgeImage.cols) + j) =
*(originalImage.data + (x * originalImage.cols) - (i *
originalImage.cols) + (y + j));
Write to new BMP Image file
}
}
}
}
}
Take 8-bit BMP image produced from previous step
as an input. Then convert it to 12X8 8-bit BMP
image by following this algorithm. This 12x8 8-bit
BMP image is the output of the algorithm. In this
algorithm, first go to the middle row and first column
of the input image, then go to the 4 pixels upward
and check the gray value of each pixel until gray
value becomes 255 (white). After this start reading
the pixels and store the corresponding gray value into
a 8x12 matrix.
V. RESULTS AND DISCUSSIONS
Figure 1: Iris boundaries seem to be perfect circles
In the figure 1, The recognition quality can still be
improved if boundaries are found more precisely.
Note these slight imperfections when compared to
perfect circular white contours.
Figure 2: The centers of the iris inner and outer
boundaries are different
In the figure 2, the iris inner boundary and its center
are marked in red, the iris outer boundary and its
center are marked in green.
Figure 3: Gazing-Away Eyes
Figure 3 represents the gazing away eyes are
correctly detected on images, segmented and
transformed as if it were looking directly into the
camera. And in the figure 4, the iris boundaries are
not circles and not even ellipse and it is especially in
gazing-away eyes.
Figure 4: Iris boundaries are definitely not circles and
even not ellipses
VI. CONCLUSION
The Control Office Application (COA) is the
latest addition to train operations related IT
applications. The software of COA for railway
department is to be implemented on the cloud
environment. Clouds offer the opportunity to build
data observatories with data, software and expertise
together to solve problems such as those associated
with economic modeling, climate change, terrorism,
healthcare and epidemics etc. In an emerging
discipline, like cloud computing, security needs to be
analyzed more frequently. With advancement in
cloud technologies and increasing number of cloud
users, data security dimensions will continuously
increase. For the secure accessing of COA software
in the cloud, in this paper, we are introducing a new
iris detection algorithm for preventing unauthorized
access to the software. Using this algorithm, the
recognition quality can still be improved if
boundaries are found more precisely and it detects
the eyes when the centre of the outer and inner
boundaries are different, the gazing away eyes are
also correctly detected, segmented and transformed

as if it were looking directly into the camera. And
this algorithm helps to improve the security issues in
the cloud environment, when the COA software is
implemented as a software as a service in the cloud.
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Implementing Iris in the Railway Control Office Application for Secure Saas in Cloud Environment

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