Credit card fraud detection using neural network and geolocation

Gulati, Aman; Dubey, Prakash K.; MdFuzail, C; Norman, Jasmine; Ramaiah, Mangayarkarasi

doi:10.1088/1757-899x/263/4/042039

Cited by 15 publications

(8 citation statements)

References 5 publications

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“…We obtain the geographic location of the IP addresses. IP geolocation is commonly used by various Internet services and applications [19,20]. The location can be derived by looking up an IP geolocation database [21] or by active network measurements (typically latency) [22].…”

Section: Custom Locationmentioning

confidence: 99%

A Method for Cheating Indication in Unproctored On-Line Exams

Komosný

Rehman

2022

Sensors

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COVID-19 has disrupted every field of life and education is not immune to it. Student learning and examinations moved on-line on a few weeks notice, which has created a large workload for academics to grade the assessments and manually detect students’ dishonesty. In this paper, we propose a method to automatically indicate cheating in unproctored on-line exams, when somebody else other than the legitimate student takes the exam. The method is based on the analysis of the student’s on-line traces, which are logged by distance education systems. We work with customized IP geolocation and other data to derive the student’s cheating risk score. We apply the method to approx. 3600 students in 22 courses, where the partial or final on-line exams were unproctored. The found cheating risk scores are presented along with examples of indicated cheatings. The method can be used to select students for knowledge re-validation, or to compare student cheating across courses, age groups, countries, and universities. We compared student cheating risk scores between four academic terms, including two terms of university closure due to COVID-19.

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Section: Custom Locationmentioning

confidence: 99%

A Method for Cheating Indication in Unproctored On-Line Exams

Komosný

Rehman

2022

Sensors

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“…There are many applications of IP geolocation, such as where the device locations may be needed retrospectively. These include address reputation [ 14 ], phishing mitigation [ 15 ], credit card fraud [ 16 ], and forensic investigation [ 17 ].…”

Section: Related Workmentioning

confidence: 99%

Retrospective IP Address Geolocation for Geography-Aware Internet Services

Komosný

2021

Sensors

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The paper deals with the locations of IP addresses that were used in the past. This retrospective geolocation suffers from continuous changes in the Internet space and a limited availability of past IP location databases. I analyse the retrospective geolocation of IPv4 and IPv6 addresses over five years. An approach is also introduced to handle missing past IP geolocation databases. The results show that it is safe to retrospectively locate IP addresses by a couple of years, but there are differences between IPv4 and IPv6. The described parametric model of location lifetime allows us to estimate the time when the address location changed in the past. The retrospective geolocation of IP addresses has a broad range of applications, including social studies, system analyses, and security investigations. Two longitudinal use cases with the applied results are discussed. The first deals with geotargeted online content. The second deals with identity theft prevention in e-commerce.

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“…For a given input x j with w j and b, a classification boundary will be either above or below the defined hyperplane [37]. For a binary classification problem, the samples lying above the hyperplane will belong to class 1, and those who lie below will belong to class 2 [38].…”

Section: Distance Function Equationmentioning

confidence: 99%

An Effective Data Sampling Procedure for Imbalanced Data Learning on Health Insurance Fraud Detection

Kotekani

Velchamy²

2021

CIT. J. comput. inf. technol

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Fraud detection has received considerable attention from many academic research and industries worldwide due to its increasing popularity. Insurance datasets are enormous, with skewed distributions and high dimensionality. Skewed class distribution and its volume are considered significant problems while analyzing insurance datasets, as these issues increase the misclassification rates. Although sampling approaches, such as random oversampling and SMOTE can help balance the data, they can also increase the computational complexity and lead to a deterioration of model's performance. So, more sophisticated techniques are needed to balance the skewed classes efficiently. This research focuses on optimizing the learner for fraud detection by applying a Fused Resampling and Cleaning Ensemble (FusedRCE) for effective sampling in health insurance fraud detection. We hypothesized that meticulous oversampling followed with a guided data cleaning would improve the prediction performance and learner's understanding of the minority fraudulent classes compared to other sampling techniques. The proposed model works in three steps. As a first step, PCA is applied to extract the necessary features and reduce the dimensions in the data. In the second step, a hybrid combination of k-means clustering and SMOTE oversampling is used to resample the imbalanced data. Oversampling introduces lots of noise in the data. A thorough cleaning is performed on the balanced data to remove the noisy samples generated during oversampling using the Tomek Link algorithm in the third step. Tomek Link algorithm clears the boundary between minority and majority class samples and makes the data more precise and freer from noise. The resultant dataset is used by four different classification algorithms: Logistic Regression, Decision Tree Classifier, k-Nearest Neighbors, and Neural Networks using repeated 5-fold cross-validation. Compared to other classifiers, Neural Networks with FusedRCE had the highest average prediction rate of 98.9%. The results were also measured using parameters such as F1 score, Precision, Recall and AUC values. The results obtained show that the proposed method performed significantly better than any other fraud detection approach in health insurance by predicting more fraudulent data with greater accuracy and a 3x increase in speed during training.

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Credit card fraud detection using neural network and geolocation

Cited by 15 publications

References 5 publications

A Method for Cheating Indication in Unproctored On-Line Exams

A Method for Cheating Indication in Unproctored On-Line Exams

Retrospective IP Address Geolocation for Geography-Aware Internet Services

An Effective Data Sampling Procedure for Imbalanced Data Learning on Health Insurance Fraud Detection

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