Download a multi - agent fraud detection system for data communication

A MULTI - AGENT FRAUD DETECTION SYSTEM FOR DATA
COMMUNICATION NETWORKS
BY
OKOZOR NKEIRUKA PETROLINA
DEPARTMENT OF ELECTRONIC AND COMPUTER ENGINEERING
NNAMDI AZIKIWE UNIVERSITY, AKWA
AUGUST 2015
BACKGROUND OF THE STUDY
Data communication fraud occurs whenever a perpetrator uses
deception to receive services free of charge or at a reduced rate,
thereby violating existing legislation governing the economic activities of
government and its administration. It is a worldwide problem with
substantial annual revenue losses for many companies. Globally, data
communications fraud is estimated at about 100billion US dollars. In the
United State of America, data communication fraud is generally
considered to deprive network operators of approximately 5 percent of
their revenue. However, it is difficult to provide precise estimates since
some fraud may never be detected, and the operators are reluctant to
reveal figures on fraud losses. The situation can significantly be worse
for mobile operators in Africa for, as a result of fraud, they become
liable for large hard currency payments to foreign network operators.
Thus, data communication fraud is a significant problem which needs to
be addressed, detected and prevented in the strongest possible manner.
AIM AND OBJECTIVES
The aim of this thesis is to theoretical and empirical investigation of
computer intrusion fraud by the identification of channels through which
they are largely perpetrated and design of a Multi- Agent fraud detection
system for property management network.
The broad objective of this study is to design and implement a MultiAgent fraud Detection System for Data Communication Networks. The
specific Objectives include:
1. Developing and implementing a novel software for automatic data
collection and online intrusion detection that interfaces with a
property management network in an intranet setting using industry
standard protocols (ie specifically Transmission Control Protocol/
Internet protocol (TCP/IP) since the intranet uses internet technology
to operate).
2. Developing fraud detection agents using JavaScript objects to run
secretly in the background of the property (real estate) Management
Network. These shall comprise multiple intelligent agents which will
roam the network, adaptively generate models from the transaction
database and discover transactions (fraudulent activities) entering on
real-time basis which deviate significantly from the norm. The
intelligent agents collaborate to share information on suspicious
events and determine when to be more vigilant or more relaxed in a
manner that is difficult to achieve by an individual agent or monolithic
system.
3. Implementing the real Estate Management business application in
PHP language. By this, large volumes of transactions from an
organization’s transaction network can be received in real-time, and
the intelligent agents are able to derive new rules or models if they
receive new inputs. Each transaction is further captured in a
transaction table similar to log files.
4. Developing machine learning agents that use a meta-learning
technique to produce a score that labels a particular transaction as
being legal or fraudulent. The score is compared to a threshold value
that define measures such as number of successful or failed
transactions, the duration of transactions, restriction of available
services, and analysis of each other transaction-related data.
5. Developing a user interface that flags suspect transactions within
seconds, without the knowledge of the perpetrator for further
investigations and subsequent decision- making. This enables
operators to respond to fraud by detection, service denial and
prosecutions against fraudulent users.
RESEARCH METHODOLOGY
Opinions of stakeholders and data communication operators in Enugu
metropolis, capital of Enugu State of Nigeria will be gathered. The
selection of the study area was influenced by the sufficient availability of
network resources, and vibrant individuals knowledgeable on the
concept, universality and potential danger of network related frauds.
The primary sources of data includes the use of questionnaires,
observations, discussions and interviews while secondary data will be
gathered from secondary sources such as books, journals and internet
sources among others. The types of primary data to be collected
include characteristics of stakeholders and operators in Enugu, network
fraud dynamics, fraud detection techniques in place, changing patterns
of fraudsters, in the study area, among others. Example of secondary
data to be collected include age distribution, educational qualification
and assessment of levels of respondents, among others
The individuals in the metropolis will be grouped into two main strata
(network operators and stakeholders) which exhibit definite
characteristics such as age and educational levels. The simple random
sampling method will then be use to select individual from stratum.
EXPECTED OUTCOMES
Successful completion of this research work will achieve the following
implemented sub- system:
1. Mobile and stationary data gathering agents that collect system logs
and audit data and render them into common format.
2. Low level agents that monitor and classify ongoing activities, classify
events, and pass on this information to higher level agents and to
each other.
3. High level agents would provide a high- level intrusion detector,
able to analyze intrusions over the whole system, execute counter
measures, and support the system administration in their pursuit of
attackers.
4. Multiple intelligent agents that use machine learning to acquire
predictive rules for intrusion detection from system logs and audit
data identify and react to coordinated intrusions on multiple
subsystems.
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