Triple symmetric key cryptosystem for data security

Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences

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.

/pdf/identifying-online-credit-card-fraud-using-artificial-immune-4xibhcxym7.pdf

Identifying online credit card fraud using Artificial Immune Systems

The most accepted payment mode is credit card for both online and offline in today's world, it provides cashless shopping at every shop in all countries. It will be the most convenient way to do online shopping, paying bills etc. Hence, risks of fraud transaction using credit card has also been increasing. In the existing credit card fraud detection business processing system, fraudulent transaction will be detected after transaction is done. It is difficult to find out fraudulent and regarding loses will be barred by issuing authorities. Hidden Markov Model is the statistical tools for engineer and scientists to solve various problems. In this paper, it is shown that credit card fraud can be detected using Hidden Markov Model during transactions. Hidden Markov Model helps to obtain a high fraud coverage combined with a low false alarm rate.

/pdf/study-of-hidden-markov-model-in-credit-card-fraudulent-3omd3911ka.pdf

Triple symmetric key cryptosystem for data security

References

Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences

Identifying online credit card fraud using Artificial Immune Systems

Study of Hidden Markov Model in Credit Card Fraudulent Detection

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