Cyberattack Ramifications, The Hidden Cost of a Security Breach

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.

On the Study of Multi-Gas Data Acquisition System Based on PCI1711 Card

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.

Exploring the Evolution of Passenger Flow and Travel Time Reliability with the Expanding Process of Metro System Using Smartcard Data

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.

Credit Risk Transfer and the Performance of Commercial Banks --Based on the Panel Data

Based on the panel data, we analyze the US commercial banks＇ CRT. According to the study, we find that the introduction of CRT will increase the level of banks＇ liquid risk. The performance of bank mainly is that its supervision and review of risk will drop, based on the impact of asymmetric information, commercial Banks transfer the bad loans to investors. Through the analysis we can see that after the transfer of credit risk in commercial bank did not increase income and reduce risk. Because commercial Banks can extend more bad loans to expand its lending scale, and bad loans will increase the bank overall risk.

A Self-Adapting and Efficient Dandelion Algorithm and Its Application to Feature Selection for Credit Card Fraud Detection

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.

Credit Card Fraud Detection on Original European Credit Card Holder Dataset Using Ensemble Machine Learning Technique

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.

Research on the Development and Application of Campus Card in Colleges and Universities

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.

A Credit Card Fraud Model Prediction Method Based on Penalty Factor Optimization AWTadaboost

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.

A Credit Card Fraud Detection Model Based on Multi-Feature Fusion and Generative Adversarial Network

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.

Research and Implementation of Credit Investigation Sharing Platform Based on Double Blockchain

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.

Probability Theory Predicts That Chunking into Groups of Three or Four Items Increases the Short-Term Memory Capacity

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 &#177?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.

Value of big data to finance:observations on an internet credit Service Company in China

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.

Comparative Analysis of Metro Passengers’Mobility Patterns and Jobs-housing Balance of Metropolitan

The advent of the big data era has provided many types of transportation datasets,such as metro smart card data,for studying residents’mobility and understanding how their mobility has been shaped and is shaping the urban space.In this paper,we use metro smart card data from two Chinese metropolises,Shanghai and Shenzhen.Five metro mobility indicators are introduced,and association rules are established to explore the mobility patterns.The proportion of people entering and exiting the station is used to measure the jobs-housing balance.It is found that the average travel distance and duration of Shanghai passengers are higher than those of Shenzhen,and the proportion of metro commuters in Shanghai is higher than that of Shenzhen.The jobs-housing spatial relationship in Shenzhen based on metro travel is more balanced than that in Shanghai.The fundamental reason for the differences between the two cities is the difference in urban morphology.Compared with the monocentric structure of Shanghai,the polycentric structure of Shenzhen results in more scattered travel hotspots and more diverse travel routes,which helps Shenzhen to have a better jobs-housing balance.This paper fills a gap in comparative research among Chinese cities based on transportation big data analysis.The results provide support for planning metro routes,adjusting urban structure and land use to form a more reasonable metro network,and balancing the jobs-housing spatial relationship.

Design of Experimental System of USB Data Acquisition Card-based Temperature Acquisition

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.

Dynamic Programming for Estimating Acceptance Probability of Credit Card Products

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.

Analysis of passenger boarding time difference between adults and seniors based on smart card data

As an essential component of bus dwelling time, passenger boarding time has a significant impact on bus running reliability and service quality. In order to understand the passengers’ boarding process and mitigate passenger boarding time, a regression analysis framework is proposed to capture the difference and influential factors of boarding time for adult and elderly passengers based on smart card data from Changzhou. Boarding gap, the time difference between two consecutive smart card tapping records, is calculated to approximate passenger boarding time. Analysis of variance is applied to identify whether the difference in boarding time between adults and seniors is statistically significant. The multivariate regression modeling approach is implemented to analyze the influences of passenger types, marginal effects of each additional boarding passenger and bus floor types on the total boarding time at each stop. Results show that a constant difference exists in boarding time between adults and seniors even without considering the specific bus characteristics. The average passenger boarding time decreases when the number of passenger increases. The existence of two entrance steps delays the boarding process, especially for elderly passengers.

Profitable credit card business empirical analysis of factors

Since 1995, major domestic commercial banks are beginning to have a variety of credit cards issued. However, at present, China＇s relatively low profitability of the credit card business, it accounts for a smaller proportion of total bank income. By means of credit card revenue／cost structure analysis, the authors found spending and overdraft balances affecting credit card business, an important factor in profitability. At the same time, combined with a commercial bank＇s existing statistical data, using SPSS software correlation and regression analysis, the authors found that the key to improve the bank card revenue is to raise China＇s commercial banks, credit card revolving credit utilization, and expand the scale of overdraft balances.

Intelligent Agent Technologies: The Work Horse of ERP E-Commerce

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.

A Cooling System with a Fan for Thermal Management of High-Power LEDs

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.

区块链在学分银行平台中的应用研究--基于Ethereum区块链的学分银行系统的研究与实践

我国学分银行在经历《国家中长期教育改革和发展规划纲要(2010-2020年)》发布后步入迅速发展期。区块链被认为是继互联网技术之后的又一次技术大革命,其分布式部署、去中心化、数据不可篡改且可追源等特征可以在学分银行中有很好的应用。能保证学分银行学习成果信息数据的安全性,解决信任问题带来的防伪难题,有效降低平台运维成本,同时为学分银行的教育大数据的开放共享提供技术保障。