Two Way Authentication in MITM Attack to Enhance Security of E-commerce Transactions

Savita, Rupali; Datta, Unmukh

doi:10.14257/ijsia.2015.9.9.23

Cited by 3 publications

(2 citation statements)

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“…Fraud detection has also used disposable domain names that can detect fraud based on IP masking (Laurens, Rezaeighaleh, Zou, & Jusak, 2019) and detection of fraudulent transactions using a prudential multiple consensus (PMC) models (Carta, Fenu, Recupero, & Saia, 2019). Research has explored the use of blockchains to prevent fraud through implementation of cryptocurrency in payments and smart contracts (Savita & Datta, 2015) and through various antifraud systems to detect e-commerce frauds (Xie et al. , 2018).…”

Section: Prior Research In Electronic Commerce Fraud Detectionmentioning

confidence: 99%

“…Fraud detection has also used disposable domain names that can detect fraud based on IP masking (Laurens, Rezaeighaleh, Zou, & Jusak, 2019) and Frequentist vs Bayesian A/B testing detection of fraudulent transactions using a prudential multiple consensus (PMC) models (Carta, Fenu, Recupero, & Saia, 2019). Research has explored the use of blockchains to prevent fraud through implementation of cryptocurrency in payments and smart contracts (Savita & Datta, 2015) and through various antifraud systems to detect e-commerce frauds (Xie et al, 2018). Various research studies have investigated security in data transactions, passwords, networks, images and trading in e-commerce, finding that fraud can be prevented or ameliorated by using RSA (Rivest-Shamir-Adleman) encryption and Fernet cipher algorithms (Dijesh, Babu, & Vijayalakshmi, 2020).…”

Section: Prior Research In Electronic Commerce Fraud Detectionmentioning

confidence: 99%

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A comparative study of frequentist vs Bayesian A/B testing in the detection of E-commerce fraud

Westland

2022

JEBDE

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PurposeThis paper tests whether Bayesian A/B testing yields better decisions that traditional Neyman-Pearson hypothesis testing. It proposes a model and tests it using a large, multiyear Google Analytics (GA) dataset.Design/methodology/approachThis paper is an empirical study. Competing A/B testing models were used to analyze a large, multiyear dataset of GA dataset for a firm that relies entirely on their website and online transactions for customer engagement and sales.FindingsBayesian A/B tests of the data not only yielded a clear delineation of the timing and impact of the intellectual property fraud, but calculated the loss of sales dollars, traffic and time on the firm’s website, with precise confidence limits. Frequentist A/B testing identified fraud in bounce rate at 5% significance, and bounces at 10% significance, but was unable to ascertain fraud at the standard significance cutoffs for scientific studies.Research limitations/implicationsNone within the scope of the research plan.Practical implicationsBayesian A/B tests of the data not only yielded a clear delineation of the timing and impact of the IP fraud, but calculated the loss of sales dollars, traffic and time on the firm’s website, with precise confidence limits.Social implicationsBayesian A/B testing can derive economically meaningful statistics, whereas frequentist A/B testing only provide p-value’s whose meaning may be hard to grasp, and where misuse is widespread and has been a major topic in metascience. While misuse of p-values in scholarly articles may simply be grist for academic debate, the uncertainty surrounding the meaning of p-values in business analytics actually can cost firms money.Originality/valueThere is very little empirical research in e-commerce that uses Bayesian A/B testing. Almost all corporate testing is done via frequentist Neyman-Pearson methods.

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