Methods for Increasing Creditability of Anomaly Detection System

Based on Bayes' theorem we point out that the false positive rate must be lower than the intrusion base rate in order to make the Alarm Credibility Probability of the intrusion detection system exceed 50%. We present the methods that have been used in our developing intrusion detection system AIIDS (artificial immune intrusion detection systems) to increase the creditability of anomaly detection system. These methods include increasing the regularities of the system call trace by use of Hidden Markov Model (HMM), making every antibody or detector has finite lifetime, offering the detector a co-stimulate signal to illustrate whether there is damage in the system according to the integrity, confidentiality, or availability of the system resource.

Beyond authorship: Analyzing contributions in PLOS ONE and the challenges of appropriate attribution

Purpose:This study aims to evaluate the accuracy of authorship attributions in scientific publications,focusing on the fairness and precision of individual contributions within academic works.Design/methodology/approach:The study analyzes 81,823 publications from the journal PLOS ONE,covering the period from January 2018 to June 2023.It examines the authorship attributions within these publications to try and determine the prevalence of inappropriate authorship.It also investigates the demographic and professional profiles of affected authors,exploring trends and potential factors contributing to inaccuracies in authorship.Findings:Surprisingly,9.14%of articles feature at least one author with inappropriate authorship,affecting over 14,000 individuals(2.56%of the sample).Inappropriate authorship is more concentrated in Asia,Africa,and specific European countries like Italy.Established researchers with significant publication records and those affiliated with companies or nonprofits show higher instances of potential monetary authorship.Research limitations:Our findings are based on contributions as declared by the authors,which implies a degree of trust in their transparency.However,this reliance on self-reporting may introduce biases or inaccuracies into the dataset.Further research could employ additional verification methods to enhance the reliability of the findings.Practical implications:These findings have significant implications for journal publishers,Beyond authorship:Analyzing contributions in PLOS ONE and Maddi,A.,&the challenges of appropriate attribution highlighting the necessity for robust control mechanisms to ensure the integrity of authorship attributions.Moreover,researchers must exercise discernment in determining when to acknowledge a contributor and when to include them in the author list.Addressing these issues is crucial for maintaining the credibility and fairness of academic publications.Originality/value:This study contributes to an understanding of critical issues within academic authorship,shedding light on the prevalence and impact of inappropriate authorship attributions.By calling for a nuanced approach to ensure accurate credit is given where it is due,the study underscores the importance of upholding ethical standards in scholarly publishing.

Effects of formal credit on pastoral household expense: Evidence from the Qinghai-Xizang Plateau of China

Formal credit is critical in agricultural production,allowing more expenditure and productive input,thereby improving farmers'welfare.In pastoral China,formal financial institutions are gradually increasing.However,a limited understanding remains of how formal credit affects herders'household expenses.Based on a survey of 544 herders from the Qinghai-Xizang Plateau of China,this study adopted the propensity score matching approach to identify the effect of formal credit on herders'total household expenses,daily expenses,and productive expenses.The results found that average age,grassland mortgage,and other variables significantly affected herders'participation in formal credit.Formal credit could significantly improve household expenses,especially productive expenses.A heterogeneity analysis showed that formal credit had a greater impact on the household total expense for those at higher levels of wealth;however,it significantly affected the productive expense of herders at lower wealth levels.Moreover,the mediating effect indicated that formal credit could affect herders'household income,thus influencing their household expenses.Finally,this study suggests that policies should improve herders'accessibility to formal credit.

Improving Federated Learning through Abnormal Client Detection and Incentive

Data sharing and privacy protection are made possible by federated learning,which allows for continuous model parameter sharing between several clients and a central server.Multiple reliable and high-quality clients must participate in practical applications for the federated learning global model to be accurate,but because the clients are independent,the central server cannot fully control their behavior.The central server has no way of knowing the correctness of the model parameters provided by each client in this round,so clients may purposefully or unwittingly submit anomalous data,leading to abnormal behavior,such as becoming malicious attackers or defective clients.To reduce their negative consequences,it is crucial to quickly detect these abnormalities and incentivize them.In this paper,we propose a Federated Learning framework for Detecting and Incentivizing Abnormal Clients(FL-DIAC)to accomplish efficient and security federated learning.We build a detector that introduces an auto-encoder for anomaly detection and use it to perform anomaly identification and prevent the involvement of abnormal clients,in particular for the anomaly client detection problem.Among them,before the model parameters are input to the detector,we propose a Fourier transform-based anomaly data detectionmethod for dimensionality reduction in order to reduce the computational complexity.Additionally,we create a credit scorebased incentive structure to encourage clients to participate in training in order tomake clients actively participate.Three training models(CNN,MLP,and ResNet-18)and three datasets(MNIST,Fashion MNIST,and CIFAR-10)have been used in experiments.According to theoretical analysis and experimental findings,the FL-DIAC is superior to other federated learning schemes of the same type in terms of effectiveness.

Pricing Catastrophe Options with Credit Risk in a Regime-Switching Model

In this paper,we consider the price of catastrophe options with credit risk in a regime-switching model.We assume that the macroeconomic states are described by a continuous-time Markov chain with a finite state space.By using the measure change technique,we derive the price expressions of catastrophe put options.Moreover,we conduct some numerical analysis to demonstrate how the parameters of the model affect the price of the catastrophe put option.

Evolutionary analysis of green credit and automobile enterprises under the mechanism of dynamic reward and punishment based on government regulation

To explore the green development of automobile enterprises and promote the achievement of the“dual carbon”target,based on the bounded rationality assumptions,this study constructed a tripartite evolutionary game model of gov-ernment,commercial banks,and automobile enterprises;introduced a dynamic reward and punishment mechanism;and analyzed the development process of the three parties’strategic behavior under the static and dynamic reward and punish-ment mechanism.Vensim PLE was used for numerical simulation analysis.Our results indicate that the system could not reach a stable state under the static reward and punishment mechanism.A dynamic reward and punishment mechanism can effectively improve the system stability and better fit real situations.Under the dynamic reward and punishment mechan-ism,an increase in the initial probabilities of the three parties can promote the system stability,and the government can im-plement effective supervision by adjusting the upper limit of the reward and punishment intensity.Finally,the implementa-tion of green credit by commercial banks plays a significant role in promoting the green development of automobile enter-prises.

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.

Research on Early Warning of Banking Crises from the Perspective of Credit Structures

The relationship between credit expansion and banking crises is complex and cannot be fully explained by total credit alone.A systematic analysis of the relationship between the amount and structure of total credit and banking crises is important for an objective prediction of the influence of potential financial risks.This paper,drawing on data from 15 selected countries,delves into the power of credit indicators in the early warning of banking crises from the perspectives of industrial structure,sector structure,and term structure of credit.Various machine learning methods were used,including Logistic Regression,Random Forest,Decision Tree,Support Vector Machine(SVM),Bagging,and Boosting models.The empirical findings indicate that credit expansion plays a crucial role in triggering banking crises.However,total credit is better suited for the early warning of short-term banking crises,whereas credit structure is more useful for the early warning of long-term banking crises.Moreover,in an early warning system,identifying key early warning indicators is more meaningful than merely increasing the number of indicators.Machine learning can somewhat enhance the early warning power,but it may not always be robust.Therefore,more attention should be paid to potential systemic banking crises resulting from an imbalance in credit structure while regulating the total credit threshold.

Construction of BDS Spatiotemporal Information Agricultural Product Digital Credit System

Spatiotemporal information,positioning and navigation services have become important elements of new type infrastructure.The rapid development of global digital trade provides a large-scale application scenario for the use of Beidou Navigation Satellite System(BDS)spatiotemporal information to support the certification of origin of agricultural products.The BDS spatiotemporal information agricultural product digital credit system uses such modules as BDS,spatiotemporal information collection,spatiotemporal coding,and spatiotemporal blockchain.It incorporates multi-level joint supervision mechanisms such as government,associations,and users.Starting from the initial production link of agricultural products,it realizes the correspondence and locking of online and offline products,effectively improves the integrity and credibility of information in the production process,finished product quality and circulation process of products,effectively manages the green production and anti-channel conflicts of producers,and provides credible information for consumers,thus realizing the digital credit certification of products.The successful practice of characteristic agricultural products in Yunnan Province has verified the application ability of the BDS spatiotemporal information agricultural product digital credit system.This system has played a significant role in promoting the online and offline locking,credible information,effective supervision and high quality and high price of characteristic agricultural products from the field.The BDS provides services for global digital trade and contributes to the further enhancement of the global application scale of GNSS.

Real-Time Fraud Detection Using Machine Learning

Credit card fraud remains a significant challenge, with financial losses and consumer protection at stake. This study addresses the need for practical, real-time fraud detection methodologies. Using a Kaggle credit card dataset, I tackle class imbalance using the Synthetic Minority Oversampling Technique (SMOTE) to enhance modeling efficiency. I compare several machine learning algorithms, including Logistic Regression, Linear Discriminant Analysis, K-nearest Neighbors, Classification and Regression Tree, Naive Bayes, Support Vector, Random Forest, XGBoost, and Light Gradient-Boosting Machine to classify transactions as fraud or genuine. Rigorous evaluation metrics, such as AUC, PRAUC, F1, KS, Recall, and Precision, identify the Random Forest as the best performer in detecting fraudulent activities. The Random Forest model successfully identifies approximately 92% of transactions scoring 90 and above as fraudulent, equating to a detection rate of over 70% for all fraudulent transactions in the test dataset. Moreover, the model captures more than half of the fraud in each bin of the test dataset. SHAP values provide model explainability, with the SHAP summary plot highlighting the global importance of individual features, such as “V12” and “V14”. SHAP force plots offer local interpretability, revealing the impact of specific features on individual predictions. This study demonstrates the potential of machine learning, particularly the Random Forest model, for real-time credit card fraud detection, offering a promising approach to mitigate financial losses and protect consumers.

Consideration of the Application and Risk Prevention of VAT Tax Rebate Policy for Construction Enterprises at the End of the Period

The“Announcement on Deepening the Value-Added Tax Reform”clearly outlines the preferential policy regarding incremental retention tax rebates.With the advancement of value-added tax(VAT)reform and the improvement of VAT legislation in China,VAT tax planning for construction enterprises,particularly related to retained tax credits,has become routine.This paper,focusing on the characteristics of construction enterprises,analyzes VAT retained tax credits at the end of the period,the status of tax refunds,practical issues,and related processes,and offers suggestions for policy application and risk prevention.

The Macroeconomic Impact of Internet Finance

This paper investigates the macroeconomic impacts of Internet finance,highlighting its advantages and challenges.Internet finance,a fusion of Internet technology with traditional financial practices,introduces innovative models for global asset management,capital financing,payments,investments,and intermediary services.While it enhances the accessibility and efficiency of financial services,it also introduces new risks,such as higher credit default rates.This study explores how Internet finance contributes to financial inclusivity and macroeconomic growth yet poses potential threats to traditional financial stability.The dual aspects of Internet finance are analyzed:its application in existing processes and its capacity to generate novel business models.Furthermore,the paper proposes strategic responses to mitigate the negative impacts of Internet finance,mainly focusing on risk management and regulatory improvements to safeguard economic stability.

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.

模糊Credit Metrics模型及其在信用风险评估中的应用

在经典的Credit Metrics模型中,通常假设具有相同信用评级的债务人的贷款是同质的,即具有相同的信用等级转移概率,且在实际应用中这种概率难以精确估计。这些局限性大大限制了该方法在实际中的应用。为了更好地描述和处理关于信用等级转移的模糊信息,借鉴经典Credit Metrics模型的思想,提出了模糊Credit Metrics模型,通过将传统的确定的信用转移矩阵模糊化,从而利用二型模糊变量来对信贷资产的远期价值进行描述和刻画,在此基础上对信贷资产的期望值和在险价值等量化指标进行计算,为基于不确定信息或专家主观判断的信用风险评估提供了一种系统的有效方法。

信用类债券的政府信用及违约承担机制研究

建立信用违约承担机制是转变政府一再为信用类债券违约兜底的前提,也是债券信用风险得以合理分散的根本途径。通过对中国信用债市场发展现状、发展原因和国家政策等方面的分析,揭示出信用债市场目前存在的突出问题:信用债以政府信用为主导、信用风险依然集聚在银行体系。针对以上问题提出解决方案,尝试建立以信用违约互换(CDS)为中心、专业评级机构、信息披露制度和信用债券合理定价为支撑的信用违约承担机制。

财产保险公司代理人信用风险的度量

随着新巴塞尔资本协议的出台,如何对保险公司信用风险进行度量开始成为业界关注的问题。为了量化信用风险,实现更好的风险管理,稳定公司的偿付水平,文章对财产保险公司的信用风险进行了分析,并借鉴银行信用风险管理模型Credit risk+对代理人信用风险进行了实证分析,得出了公司抵御该风险所需要的经济资本量。

Xen虚拟CPU空闲调度算法

在Xen虚拟化环境下,Credit调度算法是非抢占式调度算法,当虚拟CPU空时它不会将空闲状态信息通知给Xen,因此不会放弃物理CPU的使用权.虽然已有文献提出在虚拟CPU空闲时的处理方法,但它依然存在很多问题,例如空闲虚拟CPU的空闲时间还存在浪费的现象、没有考虑特权Service OS的空闲状态和虚拟机空闲状态判断不准确等,这造成很多不必要的性能损失.针对这样的问题,在Credit算法的基础上提出了虚拟CPU空闲调度算法,虚拟CPU空闲状态接收模块接收到的虚拟CPU空闲通知,动态调整该虚拟机的虚拟CPU的credit值,并将空闲的CPU时间分配给调度队列中其他的虚拟CPU使用.同时,根据该虚拟机的虚拟CPU的平均空闲率,重新调整该虚拟机的权重,从而实现了反馈控制与虚拟机调度的动态集成,实验结果证明该调度方法使系统的整体性能得到大大提高.

基于Credit Risk+模型的互联网金融信用风险估计

文章基于Credit Risk+模型,使用互联网信贷平台四个行业的贷款数据,在不同置信水平下,对互联网金融的信用风险水平进行比较分析。结果表明,行业风险因子间协方差相等时,复合伽玛Credit Risk+模型和多元系统风险Credit Risk+模型计算结果几乎一致,与CSFB Credit Risk+模型和两阶段Credit Risk+模型相比能更好地反映贷款组合的非预期损失。行业风险因子间协方差不等时,多元系统风险Credit Risk+模型能克服其他Credit Risk+模型的缺陷,综合考量系统风险和行业风险的影响,能更好地估计贷款组合的信用风险水平。

基于Credit Portfolio View的信用风险度量模型研究

结合我国贷款企业的特点,Credit Portfolio View模型的转移矩阵中信用等级违约概率除了受宏观经济因素影响外,还受到行业因素、地区因素、规模因素以及企业所有制性质等因素影响,这些因素使得同一信用等级下的企业历史违约率统计出现差异。笔者对Credit Portfolio View模型违约因素做了宏观、行业、地区三个维度的扩展,并采用Logit模型与随机模拟相结合的方法,对模型参数进行了估计。

Xen虚拟机的虚拟CPU松弛协同调度方法

目前,Xen虚拟机调度算法均采用独立调度虚拟CPU的方式,而没有考虑虚拟机各虚拟CPU之间的协同调度关系,这会使虚拟机各个虚拟CPU之间产生很大的时钟中断数量偏差等问题,从而导致系统不稳定.为了提高系统的稳定性,基于Credit算法提出了一种比RCS(relaxed co-scheduling)算法更松弛的协同调度算法MRCS(more relaxed co-scheduling).该算法采用非抢占式协同调整方法将各个虚拟CPU相对运行的时间间隔控制在同步时间检测的上限门限值Tmax之内,同时利用同步队列中虚拟CPU优化选择调度方法和Credit算法的虚拟CPU动态迁移方法,能够更加及时地协同处理虚拟CPU,并且保证了各个物理CPU的负载均衡,有效地减少客户操作系统与VMM的环境切换次数,降低了系统开销.实验结果证明该方法不但保证了系统的稳定性,而且使系统性能得到一定程度的提升.虚拟机调度算法不仅影响虚拟机的性能,更会影响虚拟机的稳定性,致力于虚拟机调度算法的研究是一项非常有意义的工作.