The task of detecting fraud in credit card transactions is crucial to ensure the security and stability of a financial system,as well as to enforce customer confidence in digital payment systems.Historically,credit ca...The task of detecting fraud in credit card transactions is crucial to ensure the security and stability of a financial system,as well as to enforce customer confidence in digital payment systems.Historically,credit card companies have used rulebased approaches to detect fraudulent transactions,but these have proven inadequate due to the complexity of fraud strategies and have been replaced by much more powerful solutions based on machine learning or deep learning algorithms.Despite significant progress,the current approaches to fraud detection suffer from a number of limitations:for example,it is unclear whether some transaction features are more effective than others in discriminating fraudulent transactions,and they often neglect possible correlations among transactions,even though they could reveal illicit behaviour.In this paper,we propose a novel credit card fraud detection(CCFD)method based on a transaction behaviour-based hierarchical gated network.First,we introduce a feature-oriented extraction module capable of identifying key features from original transactions,and such analysis is effective in revealing the behavioural characteristics of fraudsters.Second,we design a transaction-oriented extraction module capable of capturing the correlation between users’historical and current transactional behaviour.Such information is crucial for revealing users’sequential behaviour patterns.Our approach,called transactional-behaviour-based hierarchical gated network model(TbHGN),extracts two types of new transactional features,which are then combined in a feature interaction module to learn the final transactional representations used for CCFD.We have conducted extensive experiments on a real-world credit card transaction dataset with an increase in average F1 between 1.42%and 6.53%and an improvement in average AUC between 0.63%and 2.78%over the state of the art.展开更多
In recent years,the rapid development of e-commerce exposes great vulnerabilities in online transactions for fraudsters to exploit.Credit card transactions take a salient role in nowadays’online transactions for its ...In recent years,the rapid development of e-commerce exposes great vulnerabilities in online transactions for fraudsters to exploit.Credit card transactions take a salient role in nowadays’online transactions for its obvious advantages including discounts and earning credit card points.So credit card fraudulence has become a target of concern.In order to deal with the situation,credit card fraud detection based on machine learning is been studied recently.Yet,it is difficult to detect fraudulent transactions due to data imbalance(normal and fraudulent transactions),for which Smote algorithm is proposed in order to resolve data imbalance.The assessment of Light Gradient Boosting Machine model which proposed in the paper depends much on datasets collected from clients’daily transactions.Besides,to prove the new model’s superiority in detecting credit card fraudulence,Light Gradient Boosting Machine model is compared with Random Forest and Gradient Boosting Machine algorithm in the experiment.The results indicate that Light Gradient Boosting Machine model has a good performance.The experiment in credit card fraud detection based on Light Gradient Boosting Machine model achieved a total recall rate of 99%in real dataset and fast feedback,which proves the new model’s efficiency in detecting credit card fraudulence.展开更多
Credit Card Fraud Detection(CCFD)is an essential technology for banking institutions to control fraud risks and safeguard their reputation.Class imbalance and insufficient representation of feature data relating to cr...Credit Card Fraud Detection(CCFD)is an essential technology for banking institutions to control fraud risks and safeguard their reputation.Class imbalance and insufficient representation of feature data relating to credit card transactions are two prevalent issues in the current study field of CCFD,which significantly impact classification models’performance.To address these issues,this research proposes a novel CCFD model based on Multifeature Fusion and Generative Adversarial Networks(MFGAN).The MFGAN model consists of two modules:a multi-feature fusion module for integrating static and dynamic behavior data of cardholders into a unified highdimensional feature space,and a balance module based on the generative adversarial network to decrease the class imbalance ratio.The effectiveness of theMFGAN model is validated on two actual credit card datasets.The impacts of different class balance ratios on the performance of the four resamplingmodels are analyzed,and the contribution of the two different modules to the performance of the MFGAN model is investigated via ablation experiments.Experimental results demonstrate that the proposed model does better than state-of-the-art models in terms of recall,F1,and Area Under the Curve(AUC)metrics,which means that the MFGAN model can help banks find more fraudulent transactions and reduce fraud losses.展开更多
Fraud of credit cards is a major issue for financial organizations and individuals.As fraudulent actions become more complex,a demand for better fraud detection systems is rising.Deep learning approaches have shown pr...Fraud of credit cards is a major issue for financial organizations and individuals.As fraudulent actions become more complex,a demand for better fraud detection systems is rising.Deep learning approaches have shown promise in several fields,including detecting credit card fraud.However,the efficacy of these models is heavily dependent on the careful selection of appropriate hyperparameters.This paper introduces models that integrate deep learning models with hyperparameter tuning techniques to learn the patterns and relationships within credit card transaction data,thereby improving fraud detection.Three deep learning models:AutoEncoder(AE),Convolution Neural Network(CNN),and Long Short-Term Memory(LSTM)are proposed to investigate how hyperparameter adjustment impacts the efficacy of deep learning models used to identify credit card fraud.The experiments conducted on a European credit card fraud dataset using different hyperparameters and three deep learning models demonstrate that the proposed models achieve a tradeoff between detection rate and precision,leading these models to be effective in accurately predicting credit card fraud.The results demonstrate that LSTM significantly outperformed AE and CNN in terms of accuracy(99.2%),detection rate(93.3%),and area under the curve(96.3%).These proposed models have surpassed those of existing studies and are expected to make a significant contribution to the field of credit card fraud detection.展开更多
A dandelion algorithm(DA) is a recently developed intelligent optimization algorithm for function optimization problems. Many of its parameters need to be set by experience in DA,which might not be appropriate for all...A dandelion algorithm(DA) is a recently developed intelligent optimization algorithm for function optimization problems. Many of its parameters need to be set by experience in DA,which might not be appropriate for all optimization problems. A self-adapting and efficient dandelion algorithm is proposed in this work to lower the number of DA's parameters and simplify DA's structure. Only the normal sowing operator is retained;while the other operators are discarded. An adaptive seeding radius strategy is designed for the core dandelion. The results show that the proposed algorithm achieves better performance on the standard test functions with less time consumption than its competitive peers. In addition, the proposed algorithm is applied to feature selection for credit card fraud detection(CCFD), and the results indicate that it can obtain higher classification and detection performance than the-state-of-the-art methods.展开更多
Since the coal mine in-pit personnel positioning system neither can effectively achieve the function to detect the uniqueness of in-pit coal-mine personnel nor can identify and eliminate violations in attendance manag...Since the coal mine in-pit personnel positioning system neither can effectively achieve the function to detect the uniqueness of in-pit coal-mine personnel nor can identify and eliminate violations in attendance management such as multiple cards for one person, and swiping one's cards by others in China at present. Therefore, the research introduces a uniqueness detection system and method for in-pit coal-mine personnel integrated into the in-pit coal mine personnel positioning system, establishing a system mode based on face recognition + recognition of personnel positioning card + release by automatic detection. Aiming at the facts that the in-pit personnel are wearing helmets and faces are prone to be stained during the face recognition, the study proposes the ideas that pre-process face images using the 2D-wavelet-transformation-based Mallat algorithm and extracts three face features: miner light, eyes and mouths, using the generalized symmetry transformation-based algorithm. This research carried out test with 40 clean face images with no helmets and 40 lightly-stained face images, and then compared with results with the one using the face feature extraction method based on grey-scale transformation and edge detection. The results show that the method described in the paper can detect accurately face features in the above-mentioned two cases, and the accuracy to detect face features is 97.5% in the case of wearing helmets and lightly-stained faces.展开更多
Real-time health data monitoring is pivotal for bolstering road services’safety,intelligence,and efficiency within the Internet of Health Things(IoHT)framework.Yet,delays in data retrieval can markedly hinder the eff...Real-time health data monitoring is pivotal for bolstering road services’safety,intelligence,and efficiency within the Internet of Health Things(IoHT)framework.Yet,delays in data retrieval can markedly hinder the efficacy of big data awareness detection systems.We advocate for a collaborative caching approach involving edge devices and cloud networks to combat this.This strategy is devised to streamline the data retrieval path,subsequently diminishing network strain.Crafting an adept cache processing scheme poses its own set of challenges,especially given the transient nature of monitoring data and the imperative for swift data transmission,intertwined with resource allocation tactics.This paper unveils a novel mobile healthcare solution that harnesses the power of our collaborative caching approach,facilitating nuanced health monitoring via edge devices.The system capitalizes on cloud computing for intricate health data analytics,especially in pinpointing health anomalies.Given the dynamic locational shifts and possible connection disruptions,we have architected a hierarchical detection system,particularly during crises.This system caches data efficiently and incorporates a detection utility to assess data freshness and potential lag in response times.Furthermore,we introduce the Cache-Assisted Real-Time Detection(CARD)model,crafted to optimize utility.Addressing the inherent complexity of the NP-hard CARD model,we have championed a greedy algorithm as a solution.Simulations reveal that our collaborative caching technique markedly elevates the Cache Hit Ratio(CHR)and data freshness,outshining its contemporaneous benchmark algorithms.The empirical results underscore the strength and efficiency of our innovative IoHT-based health monitoring solution.To encapsulate,this paper tackles the nuances of real-time health data monitoring in the IoHT landscape,presenting a joint edge-cloud caching strategy paired with a hierarchical detection system.Our methodology yields enhanced cache efficiency and data freshness.The corroborative numerical data accentuates the feasibility and relevance of our model,casting a beacon for the future trajectory of real-time health data monitoring systems.展开更多
The rapid growth of technology impacts all aspects of modern life, including banking and financial transactions. While these industries benefit significantly from technological advancements, they also face challenges ...The rapid growth of technology impacts all aspects of modern life, including banking and financial transactions. While these industries benefit significantly from technological advancements, they also face challenges such as credit card fraud, the most prevalent type of financial fraud. Each year, such fraud leads to billions of dollars in losses for banks, financial institutions, and customers. Although many machine learning (ML) and, more recently, deep learning (DL) solutions have been developed to address this issue, most fail to strike an effective balance between speed and performance. Moreover, the reluctance of financial institutions to disclose their fraud datasets due to reputational risks adds further challenges. This study proposes a predictive model for credit card fraud detection that leverages the unique strengths of Energy-based Restricted Boltzmann Machines (EB-RBM) and Extended Long Short-Term Memory (xLSTM) models. EB-RBM is utilized for its ability to detect new and previously unseen fraudulent patterns, while xLSTM focuses on identifying known fraud types. These models are integrated using an ensemble approach to combine their strengths, achieving a balanced and reliable prediction system. The ensemble employs a bootstrap max-voting mechanism, assigning equal voting rights to EB-RBM and xLSTM, followed by result normalization and aggregation to classify transactions as fraudulent or genuine. The model’s performance is evaluated using metrics such as AUC-ROC, AUC-PR, precision, recall, F1-score, confusion matrix, and elapsed time. Experimental results on a real-world European cardholder dataset demonstrate that the proposed approach effectively balances speed and performance, outperforming recent models in the field.展开更多
基金supported in part by the National Natural Science Foundation of China(61972241)the Natural Science Foundation of Shanghai(24ZR1427500,22ZR1427100)+1 种基金the Key Projects of Natural Science Research in Anhui Higher Education Institutions(2022AH051909)Bengbu University 2021 High-Level Scientific Research and Cultivation Project(2021pyxm04).
文摘The task of detecting fraud in credit card transactions is crucial to ensure the security and stability of a financial system,as well as to enforce customer confidence in digital payment systems.Historically,credit card companies have used rulebased approaches to detect fraudulent transactions,but these have proven inadequate due to the complexity of fraud strategies and have been replaced by much more powerful solutions based on machine learning or deep learning algorithms.Despite significant progress,the current approaches to fraud detection suffer from a number of limitations:for example,it is unclear whether some transaction features are more effective than others in discriminating fraudulent transactions,and they often neglect possible correlations among transactions,even though they could reveal illicit behaviour.In this paper,we propose a novel credit card fraud detection(CCFD)method based on a transaction behaviour-based hierarchical gated network.First,we introduce a feature-oriented extraction module capable of identifying key features from original transactions,and such analysis is effective in revealing the behavioural characteristics of fraudsters.Second,we design a transaction-oriented extraction module capable of capturing the correlation between users’historical and current transactional behaviour.Such information is crucial for revealing users’sequential behaviour patterns.Our approach,called transactional-behaviour-based hierarchical gated network model(TbHGN),extracts two types of new transactional features,which are then combined in a feature interaction module to learn the final transactional representations used for CCFD.We have conducted extensive experiments on a real-world credit card transaction dataset with an increase in average F1 between 1.42%and 6.53%and an improvement in average AUC between 0.63%and 2.78%over the state of the art.
文摘In recent years,the rapid development of e-commerce exposes great vulnerabilities in online transactions for fraudsters to exploit.Credit card transactions take a salient role in nowadays’online transactions for its obvious advantages including discounts and earning credit card points.So credit card fraudulence has become a target of concern.In order to deal with the situation,credit card fraud detection based on machine learning is been studied recently.Yet,it is difficult to detect fraudulent transactions due to data imbalance(normal and fraudulent transactions),for which Smote algorithm is proposed in order to resolve data imbalance.The assessment of Light Gradient Boosting Machine model which proposed in the paper depends much on datasets collected from clients’daily transactions.Besides,to prove the new model’s superiority in detecting credit card fraudulence,Light Gradient Boosting Machine model is compared with Random Forest and Gradient Boosting Machine algorithm in the experiment.The results indicate that Light Gradient Boosting Machine model has a good performance.The experiment in credit card fraud detection based on Light Gradient Boosting Machine model achieved a total recall rate of 99%in real dataset and fast feedback,which proves the new model’s efficiency in detecting credit card fraudulence.
基金supported by the National Key R&D Program of China(Nos.2022YFB3104103,and 2019QY1406)the National Natural Science Foundation of China(Nos.61732022,61732004,61672020,and 62072131).
文摘Credit Card Fraud Detection(CCFD)is an essential technology for banking institutions to control fraud risks and safeguard their reputation.Class imbalance and insufficient representation of feature data relating to credit card transactions are two prevalent issues in the current study field of CCFD,which significantly impact classification models’performance.To address these issues,this research proposes a novel CCFD model based on Multifeature Fusion and Generative Adversarial Networks(MFGAN).The MFGAN model consists of two modules:a multi-feature fusion module for integrating static and dynamic behavior data of cardholders into a unified highdimensional feature space,and a balance module based on the generative adversarial network to decrease the class imbalance ratio.The effectiveness of theMFGAN model is validated on two actual credit card datasets.The impacts of different class balance ratios on the performance of the four resamplingmodels are analyzed,and the contribution of the two different modules to the performance of the MFGAN model is investigated via ablation experiments.Experimental results demonstrate that the proposed model does better than state-of-the-art models in terms of recall,F1,and Area Under the Curve(AUC)metrics,which means that the MFGAN model can help banks find more fraudulent transactions and reduce fraud losses.
文摘Fraud of credit cards is a major issue for financial organizations and individuals.As fraudulent actions become more complex,a demand for better fraud detection systems is rising.Deep learning approaches have shown promise in several fields,including detecting credit card fraud.However,the efficacy of these models is heavily dependent on the careful selection of appropriate hyperparameters.This paper introduces models that integrate deep learning models with hyperparameter tuning techniques to learn the patterns and relationships within credit card transaction data,thereby improving fraud detection.Three deep learning models:AutoEncoder(AE),Convolution Neural Network(CNN),and Long Short-Term Memory(LSTM)are proposed to investigate how hyperparameter adjustment impacts the efficacy of deep learning models used to identify credit card fraud.The experiments conducted on a European credit card fraud dataset using different hyperparameters and three deep learning models demonstrate that the proposed models achieve a tradeoff between detection rate and precision,leading these models to be effective in accurately predicting credit card fraud.The results demonstrate that LSTM significantly outperformed AE and CNN in terms of accuracy(99.2%),detection rate(93.3%),and area under the curve(96.3%).These proposed models have surpassed those of existing studies and are expected to make a significant contribution to the field of credit card fraud detection.
基金supported by the Institutional Fund Projects(IFPIP-1481-611-1443)the Key Projects of Natural Science Research in Anhui Higher Education Institutions(2022AH051909)+1 种基金the Provincial Quality Project of Colleges and Universities in Anhui Province(2022sdxx020,2022xqhz044)Bengbu University 2021 High-Level Scientific Research and Cultivation Project(2021pyxm04)。
文摘A dandelion algorithm(DA) is a recently developed intelligent optimization algorithm for function optimization problems. Many of its parameters need to be set by experience in DA,which might not be appropriate for all optimization problems. A self-adapting and efficient dandelion algorithm is proposed in this work to lower the number of DA's parameters and simplify DA's structure. Only the normal sowing operator is retained;while the other operators are discarded. An adaptive seeding radius strategy is designed for the core dandelion. The results show that the proposed algorithm achieves better performance on the standard test functions with less time consumption than its competitive peers. In addition, the proposed algorithm is applied to feature selection for credit card fraud detection(CCFD), and the results indicate that it can obtain higher classification and detection performance than the-state-of-the-art methods.
基金financial supports from the National Natural Science Foundation of China (No. 51134024)the National High Technology Research and Development Program of China (No. 2012AA062203)are gratefully acknowledged
文摘Since the coal mine in-pit personnel positioning system neither can effectively achieve the function to detect the uniqueness of in-pit coal-mine personnel nor can identify and eliminate violations in attendance management such as multiple cards for one person, and swiping one's cards by others in China at present. Therefore, the research introduces a uniqueness detection system and method for in-pit coal-mine personnel integrated into the in-pit coal mine personnel positioning system, establishing a system mode based on face recognition + recognition of personnel positioning card + release by automatic detection. Aiming at the facts that the in-pit personnel are wearing helmets and faces are prone to be stained during the face recognition, the study proposes the ideas that pre-process face images using the 2D-wavelet-transformation-based Mallat algorithm and extracts three face features: miner light, eyes and mouths, using the generalized symmetry transformation-based algorithm. This research carried out test with 40 clean face images with no helmets and 40 lightly-stained face images, and then compared with results with the one using the face feature extraction method based on grey-scale transformation and edge detection. The results show that the method described in the paper can detect accurately face features in the above-mentioned two cases, and the accuracy to detect face features is 97.5% in the case of wearing helmets and lightly-stained faces.
基金supported by National Natural Science Foundation of China(NSFC)under Grant Number T2350710232.
文摘Real-time health data monitoring is pivotal for bolstering road services’safety,intelligence,and efficiency within the Internet of Health Things(IoHT)framework.Yet,delays in data retrieval can markedly hinder the efficacy of big data awareness detection systems.We advocate for a collaborative caching approach involving edge devices and cloud networks to combat this.This strategy is devised to streamline the data retrieval path,subsequently diminishing network strain.Crafting an adept cache processing scheme poses its own set of challenges,especially given the transient nature of monitoring data and the imperative for swift data transmission,intertwined with resource allocation tactics.This paper unveils a novel mobile healthcare solution that harnesses the power of our collaborative caching approach,facilitating nuanced health monitoring via edge devices.The system capitalizes on cloud computing for intricate health data analytics,especially in pinpointing health anomalies.Given the dynamic locational shifts and possible connection disruptions,we have architected a hierarchical detection system,particularly during crises.This system caches data efficiently and incorporates a detection utility to assess data freshness and potential lag in response times.Furthermore,we introduce the Cache-Assisted Real-Time Detection(CARD)model,crafted to optimize utility.Addressing the inherent complexity of the NP-hard CARD model,we have championed a greedy algorithm as a solution.Simulations reveal that our collaborative caching technique markedly elevates the Cache Hit Ratio(CHR)and data freshness,outshining its contemporaneous benchmark algorithms.The empirical results underscore the strength and efficiency of our innovative IoHT-based health monitoring solution.To encapsulate,this paper tackles the nuances of real-time health data monitoring in the IoHT landscape,presenting a joint edge-cloud caching strategy paired with a hierarchical detection system.Our methodology yields enhanced cache efficiency and data freshness.The corroborative numerical data accentuates the feasibility and relevance of our model,casting a beacon for the future trajectory of real-time health data monitoring systems.
文摘The rapid growth of technology impacts all aspects of modern life, including banking and financial transactions. While these industries benefit significantly from technological advancements, they also face challenges such as credit card fraud, the most prevalent type of financial fraud. Each year, such fraud leads to billions of dollars in losses for banks, financial institutions, and customers. Although many machine learning (ML) and, more recently, deep learning (DL) solutions have been developed to address this issue, most fail to strike an effective balance between speed and performance. Moreover, the reluctance of financial institutions to disclose their fraud datasets due to reputational risks adds further challenges. This study proposes a predictive model for credit card fraud detection that leverages the unique strengths of Energy-based Restricted Boltzmann Machines (EB-RBM) and Extended Long Short-Term Memory (xLSTM) models. EB-RBM is utilized for its ability to detect new and previously unseen fraudulent patterns, while xLSTM focuses on identifying known fraud types. These models are integrated using an ensemble approach to combine their strengths, achieving a balanced and reliable prediction system. The ensemble employs a bootstrap max-voting mechanism, assigning equal voting rights to EB-RBM and xLSTM, followed by result normalization and aggregation to classify transactions as fraudulent or genuine. The model’s performance is evaluated using metrics such as AUC-ROC, AUC-PR, precision, recall, F1-score, confusion matrix, and elapsed time. Experimental results on a real-world European cardholder dataset demonstrate that the proposed approach effectively balances speed and performance, outperforming recent models in the field.
