Developing An Artificial Immune System Model

For Cash Card Fraud Detection

This document taken from graduation thesis ,submitted at September 2014,University of Khartoum Faculty of

mathematical science –Computer Science department

Khawla O Abdelmajed ,Arwa A.Eltyeb ,Romisa E Mahjob

o.khawla77@gmail.com

Agenda Background and Problem Context. Research Aim &Objectives &Significance. Artificial Immune System (AIS) Research Methodology Developing The Model Finding of works Recommendation &Future worksReferences

Background and problem contextRecently it has been observed that, how problems in

computing and engineering are getting more complex as the two fields developed.

As result of the situation, the researchers are digging deep in biologically-inspired techniques, which mimic natural phenomenon ,absolutely no thing is like a nature system to inspire from it

the biologically-inspired techniques have a great features and potentials that motives the researchers to adopt it, like: Robustness, adaptability, and sophistication

In this context AIS are one of biological techniques ,On the other hand the Cash Card fraud are represent The complex problem in this research.

Here in Sudan With the developing of E-commerce and E-payment ,financial transactions must be secured against any attacks attempt ,therefore it’s not enough having PIN codes as a security measures for customer accounts any more. More security countermeasures needed to be forced

Research Aim &Objectives &Significance Research Aim :

To design a model based on an AIS algorithm for detecting cash card fraud problem based on cardholder’s purchase behavior.

Research Objectives:

i. To evaluate the state of the art in artificial immune system algorithms and techniques.

ii. To develop an AIS algorithm to outperform other traditional techniques in solving the e-payment fraud detections problem

Research SignificanceWhy its important to conduct the research now? E-commerce and e-payment here are in

still on the stage of development , it’s not fully been deployed yet, it would sooner be enforced according to the rapid technology changes worldwide

In order to be prepared and ready to use this technology, measures and ways must be determined to secure the future customers of this service

Artificial Immune System and Fraud Why AIS was selected from other bio-technique to

detect the Card Fraud ?Cash Card fraud are serious problem around the world

and in local area ,Cause loss of many affecting the world economics , there are several technique to detect the fraud biological technique and others.

Why Immunity -Answer

11 October

technique Detection

speed

accuracy Cost

ANN Fast Medium Expensive

GA Good Medium Inexpensi

ve

AIS Very fast Good Inexpensi

ve

Research Methodology Processes Out comes

Reviewing the Literature Criteria to select AIS Criteria to evaluate the

result based on the Fraud properties

Reviewing the AIS Selected the algorithm model

Implement the proposed model

Prepared Data – Generate Running algorithm – the

Code Getting Result

Evaluation Evaluate the result base on fraud perspective Selected Criteria ch2

comparing to other technique

Developing The Model – AIS Engineering Model

Developing The Model –NSA

The idea of Negative selection is that a set of candidate detectors is generated to match non normal patterns ,If any of the detectors set match an element in the self set or normal set it is eliminated at once

This vector is represented by a center and a radius (c , r) it is n dimensional detector.

The radius define when an entity belongs to another entity (detector or self ) that is if it was in the range defined by the radius The detector in one dimension has the spherical (circle) shape but in the dimension space it take the hyper spherical

Space in which as it appears every sphere

Developing The Model-NSA

The process of fraud detection consists of three stages

i. The stages are creating self

ii. generation of detectors

iii. detection of anomalies using NSA

NSA –Stage of Create the Self

Normalize process

Clustering Process

Create 3Dimension

Vector

Set of Self Space

Data

NSA- Generating of Detectors

Yes

Yes

Generate Random Detector

For each Candidate Detectors

Evaluate and rank base on the coverage

Move Detectors

Set of Mature Detectors

Is overlapping

Is overlapping the self

NSA- Detection Process

NSA –Class Diagram

Finding of Developing the Model

(I) the coverage of detector of the problem space can only be estimated not known for sure because the problem space is infinite, so it has to be estimated accurately .

(II) The number of iterations to depends on the coverage of the problem space. The algorithm stops and the last iteration occur when the coverage of the non- self -space is enough. For the purpose of this implementation the number of iteration is only an assumption.

(ii) The data structure used for this implementation was a an array that its element is the elements of the hyper sphere which is the three vectors that represents the three dimensions (amount purchased, time difference between transactions, location),this data structure doesn’t handle the dimensionality problem of the fraud problem .

(iii) When extending rapid miner by creating operator there should be better knowledge of the ,IOO objects used to extract the data from a process to the next.

Recommendation and Future works

Researcher recommended :Using Kd-Tree as more appropriate data Structure Coverage of detector could estimated using statistical

Method Future work: Completing the developing of Model (Getting the

Result )Using big data set in the testing phase Embedded the Model in operational system

Reference Chandrasekharan, H. C. P. B. P. R. R. K., 2012. Bio Inspired Approach as a

Problem Solving Technique. Network and Complex Systems, No.2, 2012(2225-0603 (Online)), pp. 14-21.

Dipankar Dasgupta, L. F. N., 2009. real world application. In: Immunlogical compution theory and application. 6000 Broken Sound Parkway NW, Suite 300: Auerbach Publications Taylor & Francis Group, pp. 171-182.

Dubois, D. J., 2011. Bio-inspired Self-organization Methods and Models for Software Development, Piazza Leonardo da Vinci, 32, 20133 Milano, Italy: Politecnico di Milano, Dipartimento di Elettronica e Informazione.

Jungwon Kim, A. O. a. R. E. O., 2011. Design of an Artificial Immune System as a Novel Anomaly Detectorfor combing finacial fraud in the reatail sector. Strand, London WC2R 2LS, U.K, Department of Computer Science King’s College London,.

Manoel Fernando Alonso Gadi, X. W. P. d. L., 2011. Credit Card Fraud Detection with Artificial immune system. S˜ao Paulo, SP, Brazil, Instituto de Matem´atica e Estat´ıstica.

tan, Y., 2009. Artificial Immune System and its application . In: Artificial Immune System and its application . National Laboratory on Machine Perception: s.n., pp. 3-107.

Tim French, M. B. B. ,. B., 2012. Nature-Inspired Techniques in the Context of Fraud Detection. s.l., IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS. 

Aiqiang X, Y. L. ,. X. Z., 2008. Optimization and Application of Real-valued Negative nSelection

Algorithm, Yantai 264001,China: Naval Aeronautical and Astronautical University.

Dasgupta, D., 2000. Artificial immune system and thier application, s.l.: Springer-.

Dasgupta, D., n.d. An overview of artificial immune systemsand their applications

Fabio Gonzalez, D. D. L. F. N., 2003. A Randomized Real-ValueNegative Selection Algorithm, s.lICARIS-2003.

J. Hunt, J. T. m. D. C. M. N. a. K. J., n.d. The Development of an Artificial Immune System for Real World Applications.

Ji z, d. D., 2004. real valued negative slelection with variable size detectors. Niño L2003, SpringerVerlag Berlin Heidelberg

Jungwon Kim, A. O. a. R. E. O., 2011. Design of an Artificial Immune System as a Novel Anomaly

Detectorfor combing finacial fraud in the reatail sector. Strand, London WC2R 2LS, U.K, Department of Computer Science King’s College London

THANK YOU

Hope its helpful information ,and feel free to ask each question just send an emails, and you can get copy of the thesis honestly t’s a very promising area to conduct the research on it ,just over take the limitation and challenge facing the author ,plan your methodology you will do it