Developing an Artificial Immune Model for Cash Fraud Detection
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Transcript of Developing an Artificial Immune Model for Cash Fraud Detection
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
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