BIOMETRIC APPLICATION OF INTELLIGENT AGENTS IN FAKE DOCUMENT DETECTION OF JOB APPLICANTS

International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol.2, No.1, February 2012
DOI : 10.5121/ijcseit.2012.2108 75
BIOMETRIC APPLICATION OF INTELLIGENT
AGENTS IN FAKE DOCUMENT DETECTION OF JOB
APPLICANTS
Salathiel Bogle1
and Suresh Sankaranarayanan2
1
Mona Institute of Applied Science, University of West Indies, Kingston, Jamaica
salbogleprogmer@gmail.com
2
Department of Computing, University of West Indies, Kingston, Jamaica
pessuresh@hotmail.com
ABSTRACT
The Job selection process in today’s globally competitive economy can be a daunting task for prospective
employees no matter their experience level. Although many years of research has been devoted to job
search and application resulting in good integration with information technology including the internet and
intelligent agent-based architectures, there are still many areas that need to be enhanced. Two such areas
include the quality of jobs associated with applicants in the job search by profiling the needs of employers
against the needs of prospective employees and the security and verifications schemes integrated to reduce
the instances of fraud and identity theft. The integration of mobile, intelligent agent, and cryptography
technologies provide benefits such as improved accessibility wirelessly, intelligent dynamic profiling, and
increased security. With this in mind we propose the intelligent mobile agents instead of human agents to
perform the Job search using fuzzy preferences which is been published elsewhere and application
operations incorporating the use of agents with a trust authority to establish employer trust and validate
applicant identity and accuracy. Our proposed system incorporates design methodologies to use JADE-
LEAP and Android to provide a robust, secure, user friendly solution.
KEY WORDS: M-commerce, Agents, Cryptography, JADE-LEAP, Android
1. INTRODUCTION
In today’s global economy, the challenges associated with finding a suitable job is amplified by
the technicalities associated with the Job search process which is seen by experience. Normally
when we want to apply for a job, we search the newspapers; listen to radio and television
broadcasts that may advertise vacancies and also job seekers register themselves with job site
portals such as Academickeys.com, Monster.com, Careerbuilder.com and so on. Many employers
do not register themselves with these mediums to provide full details of the job specifications but
instead post important details on their own website only. Also with the growing number of online
job search engines, segmenting the online labor market into “information islands”, make it almost
impossible for job seekers to get an overview of all relevant positions [1]. Thus feasible
approaches to improve navigation in job offers and protect the applicant’s identity are not only
important but they are necessary to help users find job offers more effectively [2]. For many
employers, the monetary and non-monetary aspects of jobs are important determinants of the

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number of applicants for jobs. Non-employed job applicants could have more difficulty in getting
a job interview than those currently employed but, once interviewed, may not face any further
difficulties in getting employment [3]. Therefore job application is a very competitive process in
which the applicant’s objectives includes successfully obtaining the job for the best negotiated
salary and the employer’s objectives includes selecting the most capable employee for the lowest
negotiated salary [3][4]. Today, the internet plays a major role in job search and application with
the launch of Online Career Center around 1993. Since then several mega job search sites have
come on stream with Monster.com, a semantic based job search site being one of the most
popular with over a million job postings at any time, 63 million job seekers per month in 2008
and over 150 million resumes[5].While it is attractive for site operators to encourage users to post
their resume and contact details online, job seekers need to exercise caution as they have no
control over where their resume will eventually be seen as their resume could potentially be
viewed by fraudsters who may use information from it to a mass and sell personal details or even
perpetrate identity theft [5][6]. Security is therefore very important in job application, and
knowing the identity of those whom you communicate with in virtual communities is essential for
understanding and establishing identity and the effects of identity deceptions and the conditions
that give rise to it [6]. It is indeed plausible that job applicants could easily fake identity or
achievements, unfairly positioning them in the job market. Also employers could be deceitful in
posting fictitious job postings as a scheme to defraud or mislead prospective applicants. With
these issues in mind, we will now look into intelligent agent technology and how agents can be
used towards job application system which is our proposed work. The paper is organized in
sections as follows. Section 2 provides details on Online Job search and Application systems with
motivation towards developing an Agent based Job Search system. Section 3 gives details on the
Intelligent Agent Technology followed by Agent based utility and Job Search theory. Section 4
talks on Intelligent Agent based Mobile Search system developed followed by Fingerprint
matching and Security Schemes. Section 5 talks on Agent based Secure Application System with
architecture details and algorithm. Section 6 gives the implementation details on JADE-LEAP
and Android 2.2. Section 7 is conclusion and future work.
2. ONLINE JOB SEARCH AND APPLICATION SYSTEMS
Job search and application is not a new area as several significant works have been done in
several key areas to modernize, improve security and increase the success and usage of these
systems. Some of the works that have inspired the conception of this research include:
‘Improving Job Search by network of professionals and companies’ research in which job
clusters were created to provide relationship between categories of professionals and company
needs and is based on a network of job offers, discovering interesting relationships between
them and clustering these to represent companies or professional. The output is a visualization
of the network of professions and companies [2].
The ‘Semantic Web-based’ recruitment research used the data exchange between employers,
applicants and job portals; and is based on a set of vocabularies in ontology which provide
shared terms to describe occupations, industrial sectors and job skills. Monster.com uses a
similar semantic web-based technology [1].
‘Agent-based Application for Supporting Job Matchmaking for Teleworkers’ is a multi-agent
system that performs job matchmaking in teleworking community focusing on the time
consuming task of searching for appropriate working partners [7]
Risk Aversion and Expected Utility theories which calibrate the relationship between risk
attitudes over small and large stakes when the expected utility hypothesis is maintained [8]
‘Open Source Java Framework for Biometric Web Authentication Based on BIOAPI’ which
provides interoperability between different software applications and devices by using the
BioAPI specification which allows software applications to communicate with a broad range
of biometric technologies [9]

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Although these and other research have contributed to the implementation of several web based
and android applications including monster.com, seek.com, academickeys.com,
careerbuilder.com, LinkUp, HireADroid, etc., there is no work that support an agent based
architecture running on JADE and Android that is capable of providing utility based ordering of
jobs, dynamic employer rating over period, fuzzy job selection rules [10][11] and secure
biometric application with applicant and employer verification which is unique in our system. So
taking these into consideration we have developed the Intelligent Agent-based Mobile Job Search
and Secure Application system where details on Intelligent agent based Job search system will be
discussed in brief and been published elsewhere But before going into system developed we need
to review details on Intelligent Agents, Job search Theory, Agent based utility and security
considerations towards application system.
3. INTELLIGENT AGENT TECHNOLOGY
Over the years there has been much controversy about the definition of an artificial intelligent
agent [12]. There exists a weaker notion of agents and a stronger more controversial notion. [13]
define an agent as “anything that can be viewed as perceiving its environment through sensors
and acting upon that environment through effectors”. Hayes-Roth (1995) agrees with this
definition but adds additional clarity by stating that “intelligent agents continuously perform three
functions: perception of dynamic conditions in the environment; action to affect conditions in the
environment; and reasoning to interpret perceptions, solve problems, draw inferences and
determine actions”. Agents should autonomously carry out activities by being independent or
self-directed and should not depend on external input only from human operator or other agents
to act manifesting social abilities when interacting with other agents (and possibly humans) via
some kind of agent-communication language [12-15]
Today artificial intelligent agents are integrated into many computer-based systems including
expert systems, job search engines, web searches, info-bots and so on. These autonomous agents
can perform time consuming, mundane and sometimes even life endangering tasks reliably,
instead of the human agent [16]. [14] describes a stronger notion of agency in which agents
process characteristics such as knowledge, belief, intention and obligation. Other attributes
applies to stronger agent notion. These include mobility where agents move around an electronic
network, veracity where agents will not knowingly communicate false information and
benevolence where the assumption is that an agent will try to do what it is asked and will not
have conflicting goals [17]
3.1 Job Search Theory
In a dynamic labor market, the process by which people find new jobs has importance to
policymakers and scholars also. Policymakers have made attempts to design training and other
programs to help match an individual’s skills with the requirements of potential employers [18].
Job-search theory attempts to propose strategies for making optimal employment decisions by
considering factors that determine individual’s demands and their prospect for finding an
acceptable job offer [19][20]
Job search models are measured in both discrete and continuous time and a simple model can be
used to represent the basic search behavior of an unemployed worker where the intent is to
maximize expected utility [20]. This research focuses on Discrete Time Job search. In Discrete
Time Job search the individual is interested in choosing a policy (i.e. a sequence of decision
rules) that determines whether or not to accept any particular job offer. The eventuality of the job
offer is referred to as the outcome and is dependent on preferences of the searcher such as skills,
pay, location of the employment opportunity, and the willingness of the employer to employ the

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searcher. The review of job search theory provides the basis for a discussion on agent-based
utility relevant to the job search process.
3.2 Agent-based Utility
In economics, utility functions are used to model preferences of agents. These functions serve as
a device for us to assign a single number to any bundle [21]. Proponents of Subjective Expected
Utility (SEU) theory have offered many axiomatisations of their models. One approach develops
utility and subjective probability as distinct concepts and provide explicit axioms which justify
the combination of these into an expected utility ranking of the strategies (options, actions,
prospects) before a decision maker [22]. In 1979 weighted utility theory was proposed by Chew
and MacCrimmon, and then refined by Chew [23]. Chew replaced the independence axiom with
weak independence and convexity axioms which led to a weighted linear representation [23].
Expected utility ‘u’ is a linear function on the simplex:
{p | p = (p1, p2 ,…, pn); ∑ pi = 1; ∀ I, pi ≥ 0}
A prospect (x1, p1; …; xn, pn) is a contract that yields outcome xi with probability pi based on the
linear function above. To simplify notation, we omit null outcomes and use (x, p) to denote the
prospect (x, p; 0, 1-p) that yields x with probability p and 0 with probability 1-p. The (riskless)
prospect that yields x with certainty is denoted by (x). Thus expectation to choose prospects is
based on
U(x1, p1; … ; xn, pn) = p1 u(x1) + … + pn u(xn).
That is, the overall utility of a prospect, denoted by U, is the expected utility of its outcomes
(Kahneman and Tversky 1979).
4. INTELLIGENT AGENT BASED MOBILE SEARCH SYSTEM
Although online job search sites have greatly improved the job acquisition process there are still
challenges in providing a qualitative approach to job search, providing a job best suited for an
employee. The main objective of the system developed [10][11] as shown in Fig.1 addresses the
following challenges:
• Intelligent agent (instead of human agent) to perform the job search operations by
interacting with employer and job search coordinator agents (Bogle & Suresh, 2011; Bogle
& Suresh, 2011).
• Agent based utility concept to provide suitability profiling based on configurable factors
such as distance from work, days and shift requirements, work environment, safety and
hazard considerations, remuneration, skillset, etc.
• Employ fuzzy preference rules, to make proper decision in getting a list of jobs
corresponding to the user desired specification.
• Enable past and current employees to profile employers based on configurable metrics
There are still some challenges in the system developed towards security concerns in the job
application process such as fraud and identity theft of Employee and Employer .So taking these
into consideration we here have proposed the application of Biometric – fingerprint matching
towards fraud and identity theft which is unique and first of its kind. But before going into the
details of system developed, we will review the Fingerprint matching and Security schemes.

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Fig. 1 Job Search Flow Architecture
3.3 Fingerprint Matching
Fingerprints are graphical flow-like ridges present on human fingers [24][25]. The use of
fingerprint for identification has been around since 6000 BC. With the increase in small
fingerprint devices integrated in mobile devices, laptops, etc. at cheaper costs, it is expected that
the use of fingerprint for personal authentication and security will increase [26]. Fingerprint
identification is based on two premises: (i) fingerprint details are permanent – based on the
anatomy and morphogenesis of friction ridge skin, and (ii) fingerprints of an individual are
unique – the notion of fingerprint individuality has been widely accepted based on inspection by
experts of millions of fingerprints [27]. [28][29] states that fingerprint matching can be separated
formally into the categories of verification and identification. Verification is the comparison of a
claimant fingerprint against an enrollee fingerprint, where the intention is that the claimant
fingerprint matches the enrollee fingerprint. Identification is the matching of an unknown
fingerprint against a database of known and catalogued fingerprints to associate the fingerprint
with an identity. This is traditionally used in the domain of forensic criminology [26] In order to
perform matching it is critical that an understanding of the structure and features of the
fingerprint is obtained.
The lines that flow in various patterns across the fingerprint are called ridges and the spaces
between ridges are valleys. The more microscopic of approaches is called minutia matching. The
two types are ridge ending and bifurcation. An ending is a feature where a ridge terminates and a
bifurcation is a feature where a ridge splits from a single path to two paths at a Y-junction. For
minutiae matching purposes a minutia has a location(x, y) and direction (additional features are
also possible). Minutia matching, because of deformations in sensed fingerprints, is an elastic
matching of point patterns without knowing their correspondences beforehand. Generally, finding
the best match between two point patterns is intractable even if minutiae are exactly located and
no deformations exist between these two points. The existence of deformations makes the minutia
matching much more difficult. Correlation matching can also be used and is also called global

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matching, simple image multiplication or image subtraction. Simplistically, the process can be
thought of as checking if the ridges correspond after aligning two fingerprints and subtracting
them [26][27]. Since fingerprints are permanent as discussed above, if they were intercepted
during communication or retrieved from an endpoint because of poor security, a perpetrator could
effectively fake their identity, pretending based on false biometrics. Therefore good security
schemes are extremely important to protect this biometric data.
3.4 Security Schemes and Considerations
Security is the attribute of a system to be safe against attacks or other interference [30][31]. Since
a person’s fingerprint is very sensitive data, it is very important that this biometric data be
protected by using cryptographic techniques [32].There are numerous cryptographic schemes and
algorithms available however this research is specifically interested in Certificate based
authentication and trust, HTTPS, and AES symmetric key encryption, discussed below.
X.509 certificate is a signed record that associates users’ identification with the cryptographic
keys and the framework postulates that everyone will obtain certificates from an official
certifying authority (CA) usually a Trusted Third Party (TTP). The public-key certificate consists
of a data part and a signature part. The data part consists of the name of an entity, the public keys
corresponding to that entity, and additional relevant information including the validity period for
the public key and so on. The signature part consists of the signature of a TTP over the data part
[33].
Hypertext transport protocol secure (HTTPS) is a technology where Secure Socket Layer (SSL)
or Transport Layer Security (TLS) is applied as a sub-layer over HTTP. HTTPS automatically
encrypts and decrypts data during communication transfer. SSL is designed to make use of TCP
to provide a reliable end-to-end secure service [34]. The two most important concepts of SSL are
the session and the connection. The specification defines these as follows:
• Connection: A connection is a transport that provides a suitable service, For SSL, such
connections are peer-to-peer relationships. The connections are transient. Every
connection is associated with a session [33][34]
• Session: An SSL session is an association between a client and a server. Sessions are
created by the Handshake Protocol. Sessions define a set of cryptography security
parameters, which can be shared among multiple connections. Sessions are used to avoid
the expensive negotiation of new security parameters for each connection [34][35]
The National Institute of Standards and Technology (NIST) worked with the cryptographic
community and developed and Advanced Encryption Standard (AES). The overall goal was to
develop a Federal Information Processing Standard (FIPS) that specifies an encryption algorithm
capable of protecting sensitive (unclassified) government information [36]. The Rijndael proposal
for AES was accepted by NIST and defined a cipher in which the block length and the key length
can be independently specified to 128, 192 or 256 bits. The AES specification uses the same three
key size alternatives but limit the block length to 128 bits. Rijndael was designed to have the
following characteristics:
• Resistance against all known attacks including Brute Force attacks
• Speed and code compactness on a wide range of platforms so that the use of the
algorithm is practical in a wide range of applications
• Design simplicity [34].
4 INTELLIGENT AGENT BASED BIOMETRIC IN SECURE APPLICATION
The literature reviewed confirms that the technologies exist to create a system that improves the
job search process using well founded techniques such as utility theorem applied to autonomous

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agents, increased accessibility through the use of mobile technology and safeguards against fraud
and identity theft by using proven security encryption and biometric techniques such as
fingerprint matching, x509 certificates, and AES encryption [36]. This section provides a design
for the construction of such a system. The architecture shown in Fig 2, integrates critical
components to enable it to work effectively. A data enabled mobile network integrated with a
local area network (LAN) is the required platform. This enables efficient agent communication
between the mobile device and the multi-agent environment. All agents in the architecture were
designed to follow the FIPA2000 architecture for Agent Communication Language (ACL)
message passing in multi-agent systems and provide the protocol for managing agent interaction
and coordination [37].Let us now discuss the design roles and responsibilities of these
components in the architecture.
Fig. 2 Job Application Flow Architecture
The Intelligent agent based Secure Application design consists of the five intelligent agents and a
trusted third party. Let us now briefly consider these agents:
Applicant Agent (AA) - The applicant agent performs the activities of a human agent for job
search and application and is a key entity in the process. The Applicant Agent primary
responsibilities are:
i. Pre-populate relevant job application fields on user screen from registration data.
ii. Capture and encrypt biometric details for secure transfer over https.
iii. Securely submit over https, job application details including encrypted fingerprint and
signature.
iv. Liaise with security agent to check if employer is third party trusted and communicate
warning to user if employer is not, also giving them the option to abort the job
application if the user desires.
v. Report job application submission status and job application number for future reference.
Employer Agent (EA) –The employer agent models some actions and responsibilities
performed by the employer. The main activities are:

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i. Interact with the Security Agent and Trusted Third Party in the employer verification
phase by providing valid certificates that are third party trusted as input for verification.
The employer trusted website certificate if from the known trusted third party, is
sufficient to mitigate costs.
ii. Sends job application details excluding biometrics as well as applicant education status
results to the human employer.
Security Agent (SA) – This agent is critical in the job application process and provides
confidentiality and some nonrepudiation by employing cryptographic functions to securely pass
data including biometric fingerprint and applicant signature from the applicant to the trusted
third party for verification. The security agent also communicates the job application status to
the AA and EA respectively and performs these functions:
i. Establish HTTPS SSL connection with the Applicant Agent for secure transfer of job
application data including biometric data.
ii. Establish secure https connection with trusted third party to verify employer certificates
using trusted third party APIs.
iii. Establish secure https connection with employer agent to obtain certificate for trust
verification.
iv. Receives applicant education status verification results and securely transfers over https
connection results to the applicant agent for presentation to the applicant.
v. Securely transfers applicant’s job application details with education status verification but
excluding applicant’s biometric data as a report to the employer agent for presentation to
the employer.
University Agent (UA) – This agent theoretically interacts on behalf of the university in the
verification process between the trusted third party and the university databases. Since each
university will have its own legal and security concerns, this abstraction is made to facilitate
varying degrees of limitations within the university that impacts the verification process. The
core responsibilities are:
i. Interact with the university database in accordance to university policy and procedures
relating to student’s confidentiality.
ii. Verify the identity of the trusted third party via its certificate to ensure it is not
communicating with a fake or unknown TTP.
iii. Securely liaise with the trusted third party to ensure only authorized applicant education
verification requests are processed.
iv. Keep a log audit of all such interactions for the university administration
Trusted Third Party (TTP) - The trusted third party for example VeriSign has the role of
securely interacting with the SA, EA and UA agents through certificate based trust schemes and
symmetric based encryption to validate biometric data, the existence of education credentials
where possible and verify the existence and trust or lack thereof of employers who post jobs to
the system. The following role is expected of the TTP:
i. Securely liaise via https with the security agent to securely obtain encrypted biometric
and education details for verification and validation.
ii. Securely liaise with the security agent via APIs to validate the employer certificates.
iii. Securely verify applicant identity by biometric data by using fingerprint matching
techniques.
iv. Securely liaise with the university agent to verify applicant’s education credentials.

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4.1 Job Application Algorithm
The following steps outline the job application process from the system architecture shown in Fig
2 and 3. The flowchart of the process is shown in Fig 4:
• Applicant securely logs-in over https and clicks apply on a job selected from the search
process.
• The applicant agent then sends a message to the security agent who checks if the logged
in user has already applied for this job. If the applicant had previously applied, their
reference number is returned and presented along with a message that they have
previously applied and their application was submitted to the employer. The process then
stops.
• If the applicant had not previously applied for the job, the security agent liaises with the
employer agent and trusted third party APIs to verify if the employer is trusted. If
employer is untrusted, the applicant is alerted by the applicant agent and allowed to
decide on stopping or proceeding with the application process.
• The applicant agent auto-complete on the job application screens, some relevant data
such as applicant’s name, address, telephone, email, etc. from the registration process.
This information is editable and can be modified by applicant at any time.
• Applicant agent collects all inputs including captured biometric fingerprint and signature
data for submission over https.
• Biometric fingerprint and signature data are encrypted using public key AES 256bit or
128bit encryption depending on the capabilities of the mobile device prior to submission
over https.
• Applicant agent submits all inputs via https to the security agent.
• The security agent updates the databases with the application details.
• The security agent liaises with the trusted third party to verify applicant’s education
details.
• The trusted third party confirms if the employer is a trusted employer. If the employer is
untrusted, the trusted third party reports to the security agent that it is unable to perform
the requested checks for security reasons as the employer is not third party trusted and
stops the verification process.
• If the employer is trusted, the trusted third party then securely liaise with the university
agent over https to obtain university copy of applicant’s fingerprint and or signature data
and relevant summary education details in the format of full-name, year and award
obtained for each award obtained.
• The university agent can choose to respond with details or deny request based on strict
university policy, or if university is primarily paper base or do not have biometric data of
students.
• If the request is denied, the trusted third party will report to security agent that it is unable
to verify the applicant education details but with a reason of university does not permit.
• If the request is serviced, the trusted third party then decrypt and verify the applicant
biometric data to confirm if the reported awards from applicant are consistent with the
university records.
• If they do not match the trusted third party reports to security agent that it is not able to
verify the applicant biometric details or the education details. If they do match and are
verifiable, the trusted third party reports to the security agent that it is able to verify the
applicant’s biometric and education details.
• The security agent then communicates verification status very succinctly to the applicant
agent allowing for zero proof of knowledge as to reason for verification incase the
applicant may be fake or fraudulent.

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• The security agent also communicates securely to the employer agent applicant details
and verification status and non-inflammatory or prejudicial reason for current status to
trusted employers only. No biometric data is transferred to the employer agent.
• The applicant agent then facilitates the presentation of this asynchronous communication
to the applicant comprising of applicant number and verification status without reason.
• The employer agent also sends an asynchronous communication to the employer of the
job application submitted by the applicant including job application reference number
and also verification status with reason to the trusted employer only.
Fig. 3 Security Flow Architecture

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Fig. 4 Job Application Flow
The details on the implementation of the agents based on the architecture presented using
JADE-LEAP in Android 2.2 has been presented in Section 4 as screenshots.
5 IMPLEMENTATION USING JADE-LEAP
The system was implemented using java development kit 1.6.0_21 (jdk1.6.0_21) as the base
runtime environment using Eclipse Helios release build 20100617-1415 with android plug-in

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enabled as the development IDE. JADE 4.0.1 was used for the Agent Platform [38] to host all
agents, control behavior and perform agent management. JADE-LEAP 4.0 was used to enable
agents on mobile devices [39], and Jade Android 1.0 enabled JADE-LEAP agents on Android
mobile devices [31]. Android 2.1 platform 8 with Google APIs SDK was used for the creation of
the Android application with Google Maps integration capabilities .The Bouncy Castle jdk16-145
lightweight JCE extensions for lightweight cryptography functions including symmetric
encryption, public key encryption, and certificate based authentication on Android mobile
devices, along with jdk1.6.0_21 Key tool were leveraged for the creation and signing of public
key certificates and for establishing the backbone of the system’s cryptographic security.
GRFingerJava 4.5 fingerprint matching API provided a means to simulate the fingerprint
matching facilities based on design architecture in Section 3 that could be used by the trusted
third party. Finally MySQL5.1 Database Server using MySQL Workbench 5.2CE was used to
create all databases for employer, university, job search & application and trusted third party.
The list of agents in the JADE environment is shown in Fig.5. The Applicant agent resides in a
separate split container and labeled with the mobile handset’s IMEI number. All other agents
reside in the main container and are identified by their abbreviated names.
Fig.5. Agents in JADE Container
5.1 Job Application Implementation
In order to apply for a job , an applicant must be securely signed in to the system over HTTPS
with a valid username and password as shown in Fig. 6.

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Fig. 6 User Log-in Screen
The applicant demographic and biometric screens are configured as mandatory. Screens that
capture supplementary data useful for the applicant digital resume profile like his personal details,
Education, Work Experience etc are optional and can be updated at any time. Let us now consider
the activities involved in the secure job application process. In this section also there are several
scenarios possible from the design but let us discuss the following four (4) scenarios and their
screenshots.
1. Employer is third party trusted
2. Employer is not third party trusted
3. Applicant details were successfully verified
4. Applicant details could not be successfully verified.
5.1.1 Employer is third party trusted
An employer is considered third party trusted if and only if their company’s existence is validated
based on trusted certificates obtained from the trusted third party. For this system the trusted third
party is VeriSign. Fig. 7 shows the trust icon obtained by a verified employer after the Security
agent securely connects to the VeriSign over HTTPS and uses APIs to validate if the Employer
certificate obtained from the employer agent is still valid and was authentic and issued by same
trusted third party. Only trusted employers will trigger an applicant details validation by VeriSign
with the University.

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Fig. 7 VeriSign trusted Employer
5.1.2 Employer is not third party trusted
If an employer does not have a valid certificate, for example their website certificate; or the
employer uses another trusted third party not supported by the system, the employer will not be
validated as third party trusted as shown in Fig.7. Applicants can still choose to submit their
applications securely to untrusted employers if they have some reason to trust these employers;
however a strong caution is given before allowing submission. This ensures legally that the
decision to give untrusted employer who could really be fake, access to their personal
demographic details only such as name, telephone, email, belongs only to the applicant.
Biometric data is never given directly to an employer and applicants cannot adjust this security
level via a configuration. On receiving the alert that the employer is untrusted, the applicant can
cancel the application process to protect their demographic data from unknown and fake
employers.
5.1.3 Building the Digital Resume
The system demonstrates data capture mechanism for customer demographics and additional
details, where portions of this data such as name, email address, telephone number, and so on is
auto-completed by the system from data captured in the registration process. All applicants must
provide the data to ensure that the employer has the most recent data on the applicant. The system
allows for the creation of an electronic resume. The sections currently captured by the system
include education details, work experience, hobbies, and references..
5.1.4 Acquiring and encrypting biometrics
No biometric data is stored on the applicant’s mobile to reduce the incident of biometric fraud in
the event the mobile device is stolen or used by multiple persons to access the system. A mobile
device with a front facing camera or fingerprint scanner is recommended. The system facilitates
the capture of a signature using the device touch screen and a stylus. The signature is then
encrypted using AES 128/256 bit encryption scheme as shown in Figs. 8 to 11. Since the
sensitivity of fingerprint data is not time bound, it is important that the fingerprint is captured,

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scan quality verified, encrypted, and transferred over HTTPS with any cache removed from the
handset. Fig. 37 and 38 demonstrates the screens for capturing fingerprint from the device camera
and encrypting using AES 256bit encryption - recommended. A weaker AES 128 bit can be used
if the device cannot support the 256 bit encryption. All encryptions for signature and fingerprint
are automatically done when transitioning to the next screen if not yet encrypted by clicking on
the encrypt button.
Fig. 8 Applicant’s Signature Fig. 9 Encrypted signature
Fig. 10 Applicant’s Fingerprint Fig. 11 Encrypted fingerprint
6.1.5 Applicant details successfully verified
In this scenario, the employer is trusted, and the applicant made good effort to provide accurate
education, work experience and reference details. The security agent then securely connects over

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HTTPS to the trusted third party VeriSign and provides biometric details along with relevant
demographics and education details to perform the applicant verification with the University
agent. VeriSign requests encrypted fingerprint and confirmation of education obtained for the
applicant from the University agent. The fingerprint is successfully matched and the degree
obtained was confirmed. Fig. 12 shows the message returned to the applicant including job
application number for reference and confirmation of details. Fig. 13 illustrates sample
communication sent to the Employer for the job application.
Fig. 12 Job Application Message to Applicant

91
Fig. 13 Job Application Sent to Employer
6.1.6 Applicant details could not be verified.
If the employer is not third party trusted, or the applicant fakes information, or the university does
not have proper databases with fingerprint, or do not allow trusted third party interaction with
university databases as a rule, the applicant details will not be verified. It is the responsibility of
the trusted third party to react gracefully and communicate these details to the security agent for
an appropriate action to be taken. In this instance, the applicant adds false education to their
electronic resume and the security agent engage the trusted third party to attempt verification of
the applicant and education details but the university agent was unable to find education details or
master degree award listed in resume and returns encrypted fingerprint and empty education
details. VeriSign verifies the fingerprint as the applicant’s fingerprint but cannot match the
education details and reports this to the security agent. Fig. 14 show the message displayed to the
applicant along with the job application number and Fig. 15 illustrates the communication sent to
the employer.
Fig. 14 Message sent to the applicant

92
Fig. 15 Employer receive application - Failed verification status
6 CONCLUSION & FUTURE WORK
To demonstrate the soundness of our proposed system, we developed a fully functional prototype
using JADE-LEAP running on Android, integrating Google Maps API, and lightweight JCE
security APIs to create our Applicant, Job Search, Security, Employer, and University Agents that
would securely communicate in a multi-agent environment and with an external third trusted
party via HTTPS and APIs to provide intelligence and services demonstrated in the screenshots
above. The system also determined if employers are trusted or not and how to verify applicants
and react using varied degrees of verification (fully verified, employer not trusted, university does
not have biometrics, records partially match, not verified and so on).The perceived drawbacks of
the system are the cost that could be associated with the use of trusted third party APIs for
employer certificate checks and also fingerprint verification. The reliance on the university to
provide applicant matching fingerprint data and education details, as some universities may not
willingly distribute student records, or even want to keep biometric details on students. The
Intelligent Agent-based Secure Application system can be improved by enhancement of
Employer interaction through mobile interfaces developed in Android. Extend android
application towards intelligent agent-based virtual secure job interview. This could form the
initial basis of a pre-interview before a face-to-face interview if needed to facilitate quick and
affordable interviews for applicants from different geographic locations for example and so on.
Last but not the least the statistical analysis of fake applicants can be recorded for mitigating such
problems in the society.
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Authors
Salathiel Bogle receive his Master’s degree in Computer Science from University of West Indies, Jamaica
in 2011. Prior to that he did Bachelor’s Degree in Computer Science from University of Technology
Jamaica. He is presently working in reputed Telecommunication industry in Jamaica i.e. Digicel as Project
Manager. He has got a Research publication in IEEE proceedings from his Master’s Dissertation. His
research interests are mainly on intelligent agents, Mobile computing.
Dr. Suresh Sankaranarayanan holds a PhD degree (2006) in Electrical Engineering with specialization in
Networking from the University of South Australia. Later he has worked as a Postdoctoral Research Fellow
and then as a Lecturer in the University of Technology, Sydney and at the University of Sydney,
respectively during 2006-08. He is the recipient of University of South Australia President Scholarship,
towards pursuing the PhD degree programme and has also bagged the IEEE travel award in 2005. He was
working as a Lecturer (Asst. Prof. status) in the Department of Computing and lead the Intelligent
Networking Research Group, in the University of West Indies, Kingston, Jamaica, during 2008-11.
He has also worked as a Professor, School of Computer Science and Engineering, Vellore Institute of
Technology (VIT University), Chennai Campus, India, for a short period during 2011. He is now working
as Associate Professor, Department of Computer & Information Systems, Institute of Technology, Brunei
(ITB – A technological university). Currently he is also functioning as a Visiting Professor, Department of
computing, Faculty of Pure & applied Science, University of West Indies, Mona Campus, Kingston-7,
Jamaica, West Indies. He has supervised 28 research students leading to M.Sc, ME, M.Phil and M.S
degrees. He has got to his credit, as on date, about 50 fully refereed research papers published in the
Proceedings of major IEEE international conferences, as Book Chapters and in International Journals. He is
also a Reviewer and Technical Committee member for a number of IEEE Conferences and Journals. He has
conducted many tutorials, workshops and also given Guest Lectures in Networking in various Universities
and Colleges. He also managed a collaborative research programme with Oakland University, Rochester,
USA. His current research interests are mainly towards ‘Mobile and Ubiquitous Computing - Wireless
Sensor Networks, Mobile Commerce, Intelligent Agents’ used in the Health, Commercial and Engineering
sectors.

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