Developing an Artificial Immune Model for Cash Fraud Detection
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Document from thesis done by Bsc students as graduation research , to develop a model that detect a cash card fraud base on the cash card holder pattern ,the technique used to detect fraud inspired from immune system
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Developing an Artificial Immune Model for Cash Fraud Detection
- 1. 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
- 2. Agenda Background and Problem Context. Research Aim &Objectives &Significance. Artificial Immune System (AIS) Research Methodology Developing The Model Finding of works Recommendation &Future works References
- 3. Background and problem context Recently 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
- 4. 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
- 5. 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
- 6. Research Significance Why 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
- 7. 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.
- 8. Why Immunity -Answer technique Detection speed accuracy Cost ANN Fast Medium Expensive GA Good Medium Inexpensive AIS Very fast Good Inexpensive
- 9. 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
- 10. Developing The Model – AIS Engineering Model
- 11. 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
- 12. 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
- 13. 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
- 14. NSA –Stage of Create the Self Normalize process Clustering Process Create 3Dimension Vector Set of Self Space Data
- 15. NSA- Generating of Detectors Yes Generate Random Yes Detector For each Candidate Detectors Evaluate and rank base on the coverage Move Detectors Is overlapp ing Set of Mature Detectors Is overlapping the self
- 18. 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.
- 19. (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.
- 20. 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
- 21. 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.
- 22. 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
- 23. 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
Editor's Notes
- The complex problem in computing could represent in pattern recognition, image processing, data mining, machine learning, and Optimizations
- The figure here describe the Card fraud evolution over 10 years
- The researcher consider bio technique and others, such as SVM ,Markove hidden model and outlier The attention on bio technique case its proofed through chapters its more appropriate method to detect the fraud , however the comparison study include ANN ,GA ,AIS with respect of criteria consist of three factors : detection speed ,Accuracy and Cost
- This table explain the frame work of the research ,the path and steps until achieve the research aim
- Problem Domain The problem domain is cash card Fraud ,Where the solution should detect when ever ID fraud has been committed efficiently in context of time and accuracy . the problem of the fraud has been review in chapter two ,where the limitation of the other solution has been stated Data representation Data of the problem domain can be represented by a binary representation or a real valued Representation , Although the binary data is easy to analyze and it is good to represents categorical data , but the problem space is continuous .So binary representation wouldn't represented such as it represents discrete data .The real valued representation has shown that it is suitable ,since it can overcome the limitation of the binary representation Affinity measures The affinity measure define whether a detector match a certain entity or not . This measure used to verify a detector in the generation of the detectors stage where it learn whether a detector detects self which in the case declared an unacceptable detector and whether it overlap with other detectors in some approaches that is concerned with the minimizing overlapping to make detectors the detection phase as will that is if a detector matched with the suspected entity it is classified as an anomaly Choosing an algorithm In choosing an algorithm of artificial immune system the algorithm suitable for the problem is chosen .Based on the discussion in chapter negative selection algorithm has been chosen since it produce detectors that detect non normal behavior and any detector that detect self would be excluded
- In the prospect of our problem the dimensions represents the fields related to the cash card transaction ,that are used to extract the patterns of the usage of the card such as amount purchased and service_id and ranges between dates The process of the Negative Selection starts with defining a set of the self set in problem space U That is after the data has been normalized . Then the set of detectors is generated ,in which it doesn't match any of the elements in the self set. This is called the generation or the training phase .The testing phase a new set of data arrived an compared to the matured detectors which the detectors that survived the generation phase
- Then every in every point are represented in the spherical vector that is represented in the center of the vector and the radius of it. The amount purchased is represented in the context of time (the month ) whereas what is considered to be an anomaly in certain month that is regular month would be considered normal in the months that is for example month in a holiday .Same as for the difference of the time between transactions Figure As for location of every coordinate point can represent a certain area in a city that is any point outside the area of the vector space is considered an anomaly. This dimensions consists with each other the n dimensional self –vector
- Generating of detectors include : Calculation of the radius of detector: D(x ,y) =√(x-y)2 Where x is the point in which the center of the detector is located and y is the center of the self. And D is the distance between them rd=D - rs ,where rs is the radius of the self ,and r d is the radius of the detector Moving detectors C (moved) = c+ (Offset ) *( c - c(nearest ))/ |(c – c(nearest))| where c(moved) is the new center of the moved detector ,offset is a the length of the movement of the movement Detector cloning C(clone) = c+rd *( c - c(nearest ))/ |(c – c(nearest))| C(clone) is the center of the new detector (clone ) .The amount (c-c(clone))is used to specify in which direction the clone would be placed . For example with it was negative it would be moved in the opposite direction Evaluation of detectors In this step the detectors will be evaluated according to two criteria .The size of the detector (the radius) and the sum of overlapping with nearest detectors. The detector with the largest size and minimum overlapping is considered the best fitted detector. The indicator of the overlapping (W) is calculated from the sum of the overlapping with all other detectors. The overlapping between two detectors is calculated as follows W (d, d’) = (exp (ebs)-1) m Where m is the number of the dimensions equal to three and ebs is the value of (rd-rd'-D/2rd)
- The detectors compare get the distance between the center of the test data element and the center of detector if it is less than the radius of the detector then it is considered an anomaly, else it is normal
- This model explains the object oriented design of the solution. Where a the self- set and the detectors and self and new transaction all presented as hyper sphere .The hyper sphere consists of three spheres that is stores as array of three vectors