Design of an artificial immune system as a novel anomaly detector for combating financial fraud in the retail sector (original) (raw)
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Information Systems Journal, 2012
Some biological phenomena offer clues to solving real-life, complex problems. Researchers have been studying techniques such as neural networks and genetic algorithms for computational intelligence and their applications to such complex problems. The problem of security management is one of the major concerns in the development of eBusiness services and networks. Recent incidents have shown that the perpetrators of cybercrimes are using increasingly sophisticated methods. Hence, it is necessary to investigate non-traditional mechanisms, such as biological techniques, to manage the security of evolving eBusiness networks and services. Towards this end, this paper investigates the use of an Artificial Immune System (AIS). The AIS emulates the mechanism of human immune systems that save human bodies from complex natural biological attacks. The paper discusses the use of AIS on one aspect of security management, viz. the detection of credit card fraud. The solution is illustrated with a case study on the management of frauds in credit card transactions, although this technique may be used in a range of security management applications in eBusiness.
Fraud Detection System based on Artificial Immune System
Nowadays, one of the most important problems for financial companies is fraud related to online transactions. It is becoming increasingly sophisticated and advanced, leading to financial losses on the part of both customers and companies. Based on this, my company was tasked with creating a fraud detection system that is scalable and adaptable to change. This research aims to create a solution that can be used to identify differences in customer behavior patterns and detect fraud. The artificial immune system model proposed in this article, combined with certain informative features, is simple to implement and can describe customer behavior patterns.
A novel model for credit card fraud detection using Artificial Immune Systems
The amount of online transactions is growing these days to a large number. A big portion of these transactions contains credit card transactions. The growth of online fraud, on the other hand, is notable, which is generally a result of ease of access to edge technology for everyone. There has been research done on many models and methods for credit card fraud prevention and detection. Artificial Immune Systems is one of them. However, organizations need accuracy along with speed in the fraud detection systems, which is not completely gained yet. In this paper we address credit card fraud detection using Artificial Immune Systems (AIS), and introduce a new model called AIS-based Fraud Detection Model (AFDM). We will use an immune system inspired algorithm (AIRS) and improve it for fraud detection. We increase the accuracy up to 25%, reduce the cost up to 85%, and decrease system response time up to 40% compared to the base algorithm. Neda Soltani Halvaiee is graduated from the College of Computer Engineering and Information Technology at Amirkabir University of Technology. Her research focuses on credit card fraud detection. She also has a background of Cloud Computing, and usage of Artificial Immune Systems. She is currently doing PhD at Amirkabir University of Technology. She will extend her study to the field of Pervasive computing during her PhD.
A New User-Based Model for Credit Card Fraud Detection Based on Artificial Immune System
In this paper we present a new model based on Artificial Immune System for credit card fraud detection. In this model, which is based on Artificial Immune Recognition System, user behavior is considered. The model puts together the two methodologies of fraud detection, namely tracking account behavior and general thresholding. The system generates normal memory cells using each user's transaction records, yet fraud memory cells are generated based on all fraudulent records. To get more accurate results, we have performed analysis on training data in order to control the number of memory cells. During the test phase each user's transaction is presented to his/her own normal memory cells, together with fraud memory cells.
Identifying online credit card fraud using Artificial Immune Systems
2010
Significant payment flows now take place on-line, giving rise to a requirement for efficient and effective systems for the detection of credit card fraud. A particular aspect of this problem is that it is highly dynamic, as fraudsters continually adapt their strategies in response to the increasing sophistication of detection systems. Hence, system training by exposure to examples of previous examples of fraudulent transactions can lead to fraud detection systems which are susceptible to new patterns of fraudulent transactions. The nature of the problem suggests that Artificial Immune Systems (AIS) may have particular utility for inclusion in fraud detection systems as AIS can be constructed which can flag `non standard' transactions without having seen examples of all possible such transactions during training of the algorithm. In this paper, we investigate the effectiveness of Artificial Immune Systems (AIS) for credit card fraud detection using a large dataset obtained from an on-line retailer. Three AIS algorithms were implemented and their performance was benchmarked against a logistic regression model. The results suggest that AIS algorithms have potential for inclusion in fraud detection systems but that further work is required to realize their full potential in this domain.
A study of artificial immune systems applied to anomaly detection
2003
Page 1. A Study of Artificial Immune Systems Applied to Anomaly Detection A Dissertation Presented for the Doctor of Philosophy Degree The University of Memphis Fabio González May 2003 i Page 2. Dedication This work is dedicated to my wife and my son: Leydi and Jacobo. ii Page 3. Acknowledgements To Dr. Dipankar Dasgupta, who introduced me to this field of research, for his support and continuous encouragement. To my Dissertation Committee members, Dr. Olfa Nasraoui, Dr.
Review of Artificial Immune System Research
2020
Artificial immune system (AIS) is a metaphorical computational intelligence system developed using ideas and theories extracted from biological immune system. It is a growing area of research attempts to bridge the divide between immunology and engineering, it exploits the mechanisms of the natural immune system including functions, principles and models in order to develop problem solving techniques. AIS is offering great diversity of problem solving algorithms and techniques. It is one of the attracting fields, which notably succeed in convincing researchers to start investigating and developing real-world models to non-linear engineering problems applied to different applications such as anomaly detection, classification, machine learning, clustering etc. In spite of those great properties of AIS, researchers continue arguing that AIS research does not yet reach the quality and importance of the other computational intelligence techniques like neural networks, DNA computation, ev...
An artificial immune system for data analysis
Biosystems, 2000
We present a simplified view of those parts of the human immune system which can be used to provide the basis for a data analysis tool. The motivation for and reasoning behind such a model is given and the desire for a 'transparent' model and meaningful visualization and interpretation techniques is noted. A minimalist formulation of an artificial immune system and some of its behaviour is described. A simple implementation and a suitable visualization technique are demonstrated using some trivial data and the famous 'iris' data set.
International Series in Operations Research & Management Science, 2010
The human immune system has numerous properties that make it ripe for exploitation in the computational domain, such as robustness and fault tolerance, and many different algorithms, collectively termed Artificial Immune Systems (AIS), have been inspired by it. Two generations of AIS are currently in use, with the first generation relying on simplified immune models and the second generation utilising interdisciplinary collaboration to develop a deeper understanding of the immune system and hence produce more complex models. Both generations of algorithms have been successfully applied to a variety of problems, including anomaly detection, pattern recognition, optimisation and robotics. In this chapter an overview of AIS is presented, its evolution is discussed, and it is shown that the diversification of the field is linked to the diversity of the immune system itself, leading to a number of algorithms as opposed to one archetypal system. Two case studies are also presented to help provide insight into the mechanisms of AIS; these are the idiotypic network approach and the Dendritic Cell Algorithm.