In this in-depth exploration, I delve into the complex implications and costs of cybersecurity breaches. Venturing beyond just the immediate repercussions, the research unearths both the overt and concealed long-term ...In this in-depth exploration, I delve into the complex implications and costs of cybersecurity breaches. Venturing beyond just the immediate repercussions, the research unearths both the overt and concealed long-term consequences that businesses encounter. This study integrates findings from various research, including quantitative reports, drawing upon real-world incidents faced by both small and large enterprises. This investigation emphasizes the profound intangible costs, such as trade name devaluation and potential damage to brand reputation, which can persist long after the breach. By collating insights from industry experts and a myriad of research, the study provides a comprehensive perspective on the profound, multi-dimensional impacts of cybersecurity incidents. The overarching aim is to underscore the often-underestimated scope and depth of these breaches, emphasizing the entire timeline post-incident and the urgent need for fortified preventative and reactive measures in the digital domain.展开更多
A data acquisition system for testing gas sensor array response to multi-gas is presented.The testing system is based on the character of the gas response of metal oxide semiconductor gas sensor array.The data acquisi...A data acquisition system for testing gas sensor array response to multi-gas is presented.The testing system is based on the character of the gas response of metal oxide semiconductor gas sensor array.The data acquisition is realized automatically through the real time controlling of the data acquisition card PCI1711.This system is highly attractive for electronic nose,which is a powerful tool for the discrimination of gases.展开更多
Metro system has experienced the global rapid rise over the past decades. However,few studies have paid attention to the evolution in system usage with the network expanding. The paper's main objectives are to ana...Metro system has experienced the global rapid rise over the past decades. However,few studies have paid attention to the evolution in system usage with the network expanding. The paper's main objectives are to analyze passenger flow characteristics and evaluate travel time reliability for the Nanjing Metro network by visualizing the smart card data of April 2014,April 2015 and April 2016. We performed visualization techniques and comparative analyses to examine the changes in system usage between before and after the system expansion. Specifically,workdays,holidays and weekends were specially segmented for analysis.Results showed that workdays had obvious morning and evening peak hours due to daily commuting,while no obvious peak hours existed in weekends and holidays and the daily traffic was evenly distributed. Besides,some metro stations had a serious directional imbalance,especially during the morning and evening peak hours of workdays. Serious unreliability occurred in morning peaks on workdays and the reliability of new lines was relatively low,meanwhile,new stations had negative effects on exiting stations in terms of reliability. Monitoring the evolution of system usage over years enables the identification of system performance and can serve as an input for improving the metro system quality.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
The proliferation of digital payment methods facilitated by various online platforms and applications has led to a surge in financial fraud,particularly in credit card transactions.Advanced technologies such as machin...The proliferation of digital payment methods facilitated by various online platforms and applications has led to a surge in financial fraud,particularly in credit card transactions.Advanced technologies such as machine learning have been widely employed to enhance the early detection and prevention of losses arising frompotentially fraudulent activities.However,a prevalent approach in existing literature involves the use of extensive data sampling and feature selection algorithms as a precursor to subsequent investigations.While sampling techniques can significantly reduce computational time,the resulting dataset relies on generated data and the accuracy of the pre-processing machine learning models employed.Such datasets often lack true representativeness of realworld data,potentially introducing secondary issues that affect the precision of the results.For instance,undersampling may result in the loss of critical information,while over-sampling can lead to overfitting machine learning models.In this paper,we proposed a classification study of credit card fraud using fundamental machine learning models without the application of any sampling techniques on all the features present in the original dataset.The results indicate that Support Vector Machine(SVM)consistently achieves classification performance exceeding 90%across various evaluation metrics.This discovery serves as a valuable reference for future research,encouraging comparative studies on original dataset without the reliance on sampling techniques.Furthermore,we explore hybrid machine learning techniques,such as ensemble learning constructed based on SVM,K-Nearest Neighbor(KNN)and decision tree,highlighting their potential advancements in the field.The study demonstrates that the proposed machine learning models yield promising results,suggesting that pre-processing the dataset with sampling algorithm or additional machine learning technique may not always be necessary.This research contributes to the field of credit card fraud detection by emphasizing the potential of employing machine learning models directly on original datasets,thereby simplifying the workflow and potentially improving the accuracy and efficiency of fraud detection systems.展开更多
With the rapid development of the construction of smart campus in Colleges and universities and the maturity of related technologies,campus card has become the most frequently used and the most frequently used core co...With the rapid development of the construction of smart campus in Colleges and universities and the maturity of related technologies,campus card has become the most frequently used and the most frequently used core component of smart campus.Based on the actual construction of onecard system in domestic universities and the author’s years of experience in campus card management,this paper makes a systematic study on the development of campus card,virtual campus card,big data application,information security and other aspects,with a view to providing effective reference for the construction of campus card in Colleges and universities.展开更多
With the popularity of online payment, how to perform creditcard fraud detection more accurately has also become a hot issue. And withthe emergence of the adaptive boosting algorithm (Adaboost), credit cardfraud detec...With the popularity of online payment, how to perform creditcard fraud detection more accurately has also become a hot issue. And withthe emergence of the adaptive boosting algorithm (Adaboost), credit cardfraud detection has started to use this method in large numbers, but thetraditional Adaboost is prone to overfitting in the presence of noisy samples.Therefore, in order to alleviate this phenomenon, this paper proposes a newidea: using the number of consecutive sample misclassifications to determinethe noisy samples, while constructing a penalty factor to reconstruct thesample weight assignment. Firstly, the theoretical analysis shows that thetraditional Adaboost method is overfitting in a noisy training set, which leadsto the degradation of classification accuracy. To this end, the penalty factorconstructed by the number of consecutive misclassifications of samples isused to reconstruct the sample weight assignment to prevent the classifierfrom over-focusing on noisy samples, and its reasonableness is demonstrated.Then, by comparing the penalty strength of the three different penalty factorsproposed in this paper, a more reasonable penalty factor is selected.Meanwhile, in order to make the constructed model more in line with theactual requirements on training time consumption, the Adaboost algorithmwith adaptive weight trimming (AWTAdaboost) is used in this paper, so thepenalty factor-based AWTAdaboost (PF_AWTAdaboost) is finally obtained.Finally, PF_AWTAdaboost is experimentally validated against other traditionalmachine learning algorithms on credit card fraud datasets and otherdatasets. The results show that the PF_AWTAdaboost method has betterperformance, including detection accuracy, model recall and robustness, thanother methods on the credit card fraud dataset. And the PF_AWTAdaboostmethod also shows excellent generalization performance on other datasets.From the experimental results, it is shown that the PF_AWTAdaboost algorithmhas better classification performance.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
As the development of the modern economy is increasingly insep-arable from credit support,the traditional credit investigation mode has yet to meet this demand.Because of the difficulties in conventional credit data s...As the development of the modern economy is increasingly insep-arable from credit support,the traditional credit investigation mode has yet to meet this demand.Because of the difficulties in conventional credit data sharing among credit investigation agencies,poor data portability,and centralized supervision,this paper proposes a data-sharing scheme for credit investigation agencies based on a double blockchain.Given the problems such as difficult data sharing,difficult recovery of damaged data,and accessible data leakage between institutions and users with non-traditional credit inves-tigation data other than credit,this paper proposes a data-sharing scheme for credit investigation subjects based on the digital envelope.Based on the above two solutions,this paper designs a double blockchain credit data-sharing plat-form based on the“public chain+alliance chain”from credit investigation agencies’and visiting subjects’perspectives.The sharing platform uses the alliance chain as the management chain to solve the problem of complex data sharing between credit bureaus and centralized supervision,uses the public chain as the use chain to solve the problem of complex data sharing between the access subject and the credit bureaus,uses the interplanetary file system and digital envelope and other technologies to solve the problem of difficult recovery of damaged data,data leakage,and other issues.After the upload test,the average upload speed reaches 80.6 M/s.The average download speed of the system is 88.7 M/s after the download test.The multi-thread stress test tests the linkage port on the system package,and the average response time for the hypertext transfer protocol(HTTP)is 0.6 ms.The system performance and security analysis show that the sharing platform can provide safe and reliable credit-sharing services for organizations and users and high working efficiency.展开更多
Short-term memory allows individuals to recall stimuli, such as numbers or words, for several seconds to several minutes without rehearsal. Although the capacity of short-term memory is considered to be 7 ±?2 ...Short-term memory allows individuals to recall stimuli, such as numbers or words, for several seconds to several minutes without rehearsal. Although the capacity of short-term memory is considered to be 7 ±?2 items, this can be increased through a process called chunking. For example, in Japan, 11-digit cellular phone numbers and 10-digit toll free numbers are chunked into three groups of three or four digits: 090-XXXX-XXXX and 0120-XXX-XXX, respectively. We use probability theory to predict that the most effective chunking involves groups of three or four items, such as in phone numbers. However, a 16-digit credit card number exceeds the capacity of short-term memory, even when chunked into groups of four digits, such as XXXX-XXXX-XXXX-XXXX. Based on these data, 16-digit credit card numbers should be sufficient for security purposes.展开更多
Background:his paper presents a case study on 100Credit,an Internet credit service provider in China.100Credit began as an IT company specializing in e-commerce recommendation before getting into the credit rating bus...Background:his paper presents a case study on 100Credit,an Internet credit service provider in China.100Credit began as an IT company specializing in e-commerce recommendation before getting into the credit rating business.The company makes use of Big Data on multiple aspects of individuals’online activities to infer their potential credit risk.Methods:Based on 100Credit’s business practices,this paper summarizes four aspects related to the value of Big Data in Internet credit services.Results:1)value from large data volume that provides access to more borrowers;2)value from prediction correctness in reducing lenders’operational cost;3)value from the variety of services catering to different needs of lenders;and 4)value from information protection to sustain credit service businesses.Conclusion:The paper also discusses the opportunities and challenges of Big Databased credit risk analysis,which needs to be improved in future research and practice.展开更多
In order to design a more efficient and more convenient temperature acquisition system, an approach combining USB data acquisition card with K type thermocouple temperature sensor is proposed under the circumstance of...In order to design a more efficient and more convenient temperature acquisition system, an approach combining USB data acquisition card with K type thermocouple temperature sensor is proposed under the circumstance of LabVIEW 2012 programming software. Firstly, the LabVIEW 2012 programming software is used to complete a temperature acquisition control program. Secondly, K type thermocouple temperature sensor is employed to transfer the temperature information. Thirdly, Then the USB data acquisition card can collect the voltage of K type thermocouple temperature sensor and convert it to a temperature scale. And, the simplification of experimental procedure can reduce the cost of development greatly. Finally, the experimental results illustrate that the range of measurement temperature is more wide and the temperature scale is more accurate.展开更多
Banks have many variants of a product which they can offer to their customers. For example, a credit card can have different interest rates. So determining which variants of a product to offer to the new customers and...Banks have many variants of a product which they can offer to their customers. For example, a credit card can have different interest rates. So determining which variants of a product to offer to the new customers and having some indication on acceptance probability will aid with the profit optimisation for the banks. In this paper, the authors look at a model for maximisation of the profit looking at the past information via implementation of the dynamic programming model with elements of Bayesian updating. Numerical results are presented of multiple variants of a credit card product with the model providing the best offer for the maximum profit and acceptance probability. The product chosen is a credit card with different interest rates.展开更多
Agents are the new defacto standard for inclusion in modules of today’s software systems such as ERP systems, mobile applications and operating systems. Agents are an integral part of today’s software design. The qu...Agents are the new defacto standard for inclusion in modules of today’s software systems such as ERP systems, mobile applications and operating systems. Agents are an integral part of today’s software design. The question is how do intelligent agents work in the specific area of ERP credit card processing e-commerce models? To answer this question, a specific area of ERP systems will be analyzed: credit card processing for merchants. One specific merchant credit card processor will be specifically investigated: EVO Merchants. This paper will research how exactly does ERP systems interact using Application Programing Interface or “API” specified by a credit card clearing house. Secure Socket Layers or SSL, and XML are discussed and elaborated on specifically how intelligent agents play such a pivotal role in ERP e-commerce systems for credit card processing.展开更多
To improve the heat dissipation of high-power light-emitting diodes (LEDs), a cooling system with a fan is proposed. In the experiment, the LEDs array of 18 W composed of 6 LEDs of 3 W is used and the room temperature...To improve the heat dissipation of high-power light-emitting diodes (LEDs), a cooling system with a fan is proposed. In the experiment, the LEDs array of 18 W composed of 6 LEDs of 3 W is used and the room temperature is 26oC. Results show that the temperature of the substrate of LEDs reaches 62oC without the fan, however, it reaches only 32oC when the best cooling condition appears. The temperature of the LEDs decreases by 30oC since the heat produced by LEDs is transferred rapidly by the fan. The experiment demonstrates that the cooling system with the fan has good performance.展开更多
Credit card fraudulent data is highly imbalanced, and it has presented an overwhelmingly large portion of nonfraudulent transactions and a small portion of fraudulent transactions. The measures used to judge the verac...Credit card fraudulent data is highly imbalanced, and it has presented an overwhelmingly large portion of nonfraudulent transactions and a small portion of fraudulent transactions. The measures used to judge the veracity of the detection algorithms become critical to the deployment of a model that accurately scores fraudulent transactions taking into account case imbalance, and the cost of identifying a case as genuine when, in fact, the case is a fraudulent transaction. In this paper, a new criterion to judge classification algorithms, which considers the cost of misclassification, is proposed, and several undersampling techniques are compared by this new criterion. At the same time, a weighted support vector machine (SVM) algorithm considering the financial cost of misclassification is introduced, proving to be more practical for credit card fraud detection than traditional methodologies. This weighted SVM uses transaction balances as weights for fraudulent transactions, and a uniformed weight for nonfraudulent transactions. The results show this strategy greatly improve performance of credit card fraud detection.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
文摘In this in-depth exploration, I delve into the complex implications and costs of cybersecurity breaches. Venturing beyond just the immediate repercussions, the research unearths both the overt and concealed long-term consequences that businesses encounter. This study integrates findings from various research, including quantitative reports, drawing upon real-world incidents faced by both small and large enterprises. This investigation emphasizes the profound intangible costs, such as trade name devaluation and potential damage to brand reputation, which can persist long after the breach. By collating insights from industry experts and a myriad of research, the study provides a comprehensive perspective on the profound, multi-dimensional impacts of cybersecurity incidents. The overarching aim is to underscore the often-underestimated scope and depth of these breaches, emphasizing the entire timeline post-incident and the urgent need for fortified preventative and reactive measures in the digital domain.
文摘A data acquisition system for testing gas sensor array response to multi-gas is presented.The testing system is based on the character of the gas response of metal oxide semiconductor gas sensor array.The data acquisition is realized automatically through the real time controlling of the data acquisition card PCI1711.This system is highly attractive for electronic nose,which is a powerful tool for the discrimination of gases.
基金Sponsored by Projects of International Cooperation and Exchange of the National Natural Science Foundation of China(Grant No.51561135003)Key Project of National Natural Science Foundation of China(Grant No.51338003)
文摘Metro system has experienced the global rapid rise over the past decades. However,few studies have paid attention to the evolution in system usage with the network expanding. The paper's main objectives are to analyze passenger flow characteristics and evaluate travel time reliability for the Nanjing Metro network by visualizing the smart card data of April 2014,April 2015 and April 2016. We performed visualization techniques and comparative analyses to examine the changes in system usage between before and after the system expansion. Specifically,workdays,holidays and weekends were specially segmented for analysis.Results showed that workdays had obvious morning and evening peak hours due to daily commuting,while no obvious peak hours existed in weekends and holidays and the daily traffic was evenly distributed. Besides,some metro stations had a serious directional imbalance,especially during the morning and evening peak hours of workdays. Serious unreliability occurred in morning peaks on workdays and the reliability of new lines was relatively low,meanwhile,new stations had negative effects on exiting stations in terms of reliability. Monitoring the evolution of system usage over years enables the identification of system performance and can serve as an input for improving the metro system quality.
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.
文摘The proliferation of digital payment methods facilitated by various online platforms and applications has led to a surge in financial fraud,particularly in credit card transactions.Advanced technologies such as machine learning have been widely employed to enhance the early detection and prevention of losses arising frompotentially fraudulent activities.However,a prevalent approach in existing literature involves the use of extensive data sampling and feature selection algorithms as a precursor to subsequent investigations.While sampling techniques can significantly reduce computational time,the resulting dataset relies on generated data and the accuracy of the pre-processing machine learning models employed.Such datasets often lack true representativeness of realworld data,potentially introducing secondary issues that affect the precision of the results.For instance,undersampling may result in the loss of critical information,while over-sampling can lead to overfitting machine learning models.In this paper,we proposed a classification study of credit card fraud using fundamental machine learning models without the application of any sampling techniques on all the features present in the original dataset.The results indicate that Support Vector Machine(SVM)consistently achieves classification performance exceeding 90%across various evaluation metrics.This discovery serves as a valuable reference for future research,encouraging comparative studies on original dataset without the reliance on sampling techniques.Furthermore,we explore hybrid machine learning techniques,such as ensemble learning constructed based on SVM,K-Nearest Neighbor(KNN)and decision tree,highlighting their potential advancements in the field.The study demonstrates that the proposed machine learning models yield promising results,suggesting that pre-processing the dataset with sampling algorithm or additional machine learning technique may not always be necessary.This research contributes to the field of credit card fraud detection by emphasizing the potential of employing machine learning models directly on original datasets,thereby simplifying the workflow and potentially improving the accuracy and efficiency of fraud detection systems.
基金(1)Project Name:Research on Key Technologies of safe campus construction based on multi-sensor big data fusionproject number:20190303096sf+3 种基金(2)Project Name:Research on Key Technologies of smart campus management platform based on big dataProject No.:18dy026(3)Research on the application of BIM based highrise building fire rescue and big data escape planning systemProject No.:2020c019-7.
文摘With the rapid development of the construction of smart campus in Colleges and universities and the maturity of related technologies,campus card has become the most frequently used and the most frequently used core component of smart campus.Based on the actual construction of onecard system in domestic universities and the author’s years of experience in campus card management,this paper makes a systematic study on the development of campus card,virtual campus card,big data application,information security and other aspects,with a view to providing effective reference for the construction of campus card in Colleges and universities.
基金This research was funded by Innovation and Entrepreneurship Training Program for College Students in Hunan Province in 2022(3915).
文摘With the popularity of online payment, how to perform creditcard fraud detection more accurately has also become a hot issue. And withthe emergence of the adaptive boosting algorithm (Adaboost), credit cardfraud detection has started to use this method in large numbers, but thetraditional Adaboost is prone to overfitting in the presence of noisy samples.Therefore, in order to alleviate this phenomenon, this paper proposes a newidea: using the number of consecutive sample misclassifications to determinethe noisy samples, while constructing a penalty factor to reconstruct thesample weight assignment. Firstly, the theoretical analysis shows that thetraditional Adaboost method is overfitting in a noisy training set, which leadsto the degradation of classification accuracy. To this end, the penalty factorconstructed by the number of consecutive misclassifications of samples isused to reconstruct the sample weight assignment to prevent the classifierfrom over-focusing on noisy samples, and its reasonableness is demonstrated.Then, by comparing the penalty strength of the three different penalty factorsproposed in this paper, a more reasonable penalty factor is selected.Meanwhile, in order to make the constructed model more in line with theactual requirements on training time consumption, the Adaboost algorithmwith adaptive weight trimming (AWTAdaboost) is used in this paper, so thepenalty factor-based AWTAdaboost (PF_AWTAdaboost) is finally obtained.Finally, PF_AWTAdaboost is experimentally validated against other traditionalmachine learning algorithms on credit card fraud datasets and otherdatasets. The results show that the PF_AWTAdaboost method has betterperformance, including detection accuracy, model recall and robustness, thanother methods on the credit card fraud dataset. And the PF_AWTAdaboostmethod also shows excellent generalization performance on other datasets.From the experimental results, it is shown that the PF_AWTAdaboost algorithmhas better classification performance.
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.
基金supported in part by the Advanced and High-level Discipline Construction Fund of Universities in Beijing(No.3201023)in part by the Beijing Electronic Science and Technology Institute of Basic Research Funds Outstanding Master Training Project(No.328202233)in part by the National First-class Undergraduate Discipline Construction of”Communication Engineering”and“Electronic Information Engineering,”and in part by the National Cryptography Science Foundation of China.
文摘As the development of the modern economy is increasingly insep-arable from credit support,the traditional credit investigation mode has yet to meet this demand.Because of the difficulties in conventional credit data sharing among credit investigation agencies,poor data portability,and centralized supervision,this paper proposes a data-sharing scheme for credit investigation agencies based on a double blockchain.Given the problems such as difficult data sharing,difficult recovery of damaged data,and accessible data leakage between institutions and users with non-traditional credit inves-tigation data other than credit,this paper proposes a data-sharing scheme for credit investigation subjects based on the digital envelope.Based on the above two solutions,this paper designs a double blockchain credit data-sharing plat-form based on the“public chain+alliance chain”from credit investigation agencies’and visiting subjects’perspectives.The sharing platform uses the alliance chain as the management chain to solve the problem of complex data sharing between credit bureaus and centralized supervision,uses the public chain as the use chain to solve the problem of complex data sharing between the access subject and the credit bureaus,uses the interplanetary file system and digital envelope and other technologies to solve the problem of difficult recovery of damaged data,data leakage,and other issues.After the upload test,the average upload speed reaches 80.6 M/s.The average download speed of the system is 88.7 M/s after the download test.The multi-thread stress test tests the linkage port on the system package,and the average response time for the hypertext transfer protocol(HTTP)is 0.6 ms.The system performance and security analysis show that the sharing platform can provide safe and reliable credit-sharing services for organizations and users and high working efficiency.
文摘Short-term memory allows individuals to recall stimuli, such as numbers or words, for several seconds to several minutes without rehearsal. Although the capacity of short-term memory is considered to be 7 ±?2 items, this can be increased through a process called chunking. For example, in Japan, 11-digit cellular phone numbers and 10-digit toll free numbers are chunked into three groups of three or four digits: 090-XXXX-XXXX and 0120-XXX-XXX, respectively. We use probability theory to predict that the most effective chunking involves groups of three or four items, such as in phone numbers. However, a 16-digit credit card number exceeds the capacity of short-term memory, even when chunked into groups of four digits, such as XXXX-XXXX-XXXX-XXXX. Based on these data, 16-digit credit card numbers should be sufficient for security purposes.
文摘Background:his paper presents a case study on 100Credit,an Internet credit service provider in China.100Credit began as an IT company specializing in e-commerce recommendation before getting into the credit rating business.The company makes use of Big Data on multiple aspects of individuals’online activities to infer their potential credit risk.Methods:Based on 100Credit’s business practices,this paper summarizes four aspects related to the value of Big Data in Internet credit services.Results:1)value from large data volume that provides access to more borrowers;2)value from prediction correctness in reducing lenders’operational cost;3)value from the variety of services catering to different needs of lenders;and 4)value from information protection to sustain credit service businesses.Conclusion:The paper also discusses the opportunities and challenges of Big Databased credit risk analysis,which needs to be improved in future research and practice.
文摘In order to design a more efficient and more convenient temperature acquisition system, an approach combining USB data acquisition card with K type thermocouple temperature sensor is proposed under the circumstance of LabVIEW 2012 programming software. Firstly, the LabVIEW 2012 programming software is used to complete a temperature acquisition control program. Secondly, K type thermocouple temperature sensor is employed to transfer the temperature information. Thirdly, Then the USB data acquisition card can collect the voltage of K type thermocouple temperature sensor and convert it to a temperature scale. And, the simplification of experimental procedure can reduce the cost of development greatly. Finally, the experimental results illustrate that the range of measurement temperature is more wide and the temperature scale is more accurate.
文摘Banks have many variants of a product which they can offer to their customers. For example, a credit card can have different interest rates. So determining which variants of a product to offer to the new customers and having some indication on acceptance probability will aid with the profit optimisation for the banks. In this paper, the authors look at a model for maximisation of the profit looking at the past information via implementation of the dynamic programming model with elements of Bayesian updating. Numerical results are presented of multiple variants of a credit card product with the model providing the best offer for the maximum profit and acceptance probability. The product chosen is a credit card with different interest rates.
文摘Agents are the new defacto standard for inclusion in modules of today’s software systems such as ERP systems, mobile applications and operating systems. Agents are an integral part of today’s software design. The question is how do intelligent agents work in the specific area of ERP credit card processing e-commerce models? To answer this question, a specific area of ERP systems will be analyzed: credit card processing for merchants. One specific merchant credit card processor will be specifically investigated: EVO Merchants. This paper will research how exactly does ERP systems interact using Application Programing Interface or “API” specified by a credit card clearing house. Secure Socket Layers or SSL, and XML are discussed and elaborated on specifically how intelligent agents play such a pivotal role in ERP e-commerce systems for credit card processing.
文摘To improve the heat dissipation of high-power light-emitting diodes (LEDs), a cooling system with a fan is proposed. In the experiment, the LEDs array of 18 W composed of 6 LEDs of 3 W is used and the room temperature is 26oC. Results show that the temperature of the substrate of LEDs reaches 62oC without the fan, however, it reaches only 32oC when the best cooling condition appears. The temperature of the LEDs decreases by 30oC since the heat produced by LEDs is transferred rapidly by the fan. The experiment demonstrates that the cooling system with the fan has good performance.
文摘Credit card fraudulent data is highly imbalanced, and it has presented an overwhelmingly large portion of nonfraudulent transactions and a small portion of fraudulent transactions. The measures used to judge the veracity of the detection algorithms become critical to the deployment of a model that accurately scores fraudulent transactions taking into account case imbalance, and the cost of identifying a case as genuine when, in fact, the case is a fraudulent transaction. In this paper, a new criterion to judge classification algorithms, which considers the cost of misclassification, is proposed, and several undersampling techniques are compared by this new criterion. At the same time, a weighted support vector machine (SVM) algorithm considering the financial cost of misclassification is introduced, proving to be more practical for credit card fraud detection than traditional methodologies. This weighted SVM uses transaction balances as weights for fraudulent transactions, and a uniformed weight for nonfraudulent transactions. The results show this strategy greatly improve performance of credit card fraud detection.
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.