Exploiting the Community Structure of Fraudulent Keywords for Fraud Detection in Web Search

Internet users heavily rely on web search engines for their intended information.The major revenue of search engines is advertisements (or ads).However,the search advertising suffers from fraud.Fraudsters generate fake traffic which does not reach the intended audience,and increases the cost of the advertisers.Therefore,it is critical to detect fraud in web search.Previous studies solve this problem through fraudster detection (especially bots) by leveraging fraudsters' unique behaviors.However,they may fail to detect new means of fraud,such as crowdsourcing fraud,since crowd workers behave in part like normal users.To this end,this paper proposes an approach to detecting fraud in web search from the perspective of fraudulent keywords.We begin by using a unique dataset of 150 million web search logs to examine the discriminating features of fraudulent keywords.Specifically,we model the temporal correlation of fraudulent keywords as a graph,which reveals a very well-connected community structure.Next,we design DFW (detection of fraudulent keywords) that mines the temporal correlations between candidate fraudulent keywords and a given list of seeds.In particular,DFW leverages several refinements to filter out non-fraudulent keywords that co-occur with seeds occasionally.The evaluation using the search logs shows that DFW achieves high fraud detection precision (99％) and accuracy (93％).A further analysis reveals several typical temporal evolution patterns of fraudulent keywords and the co-existence of both bots and crowd workers as frandsters for web search fraud.     

Credit card fraud detection: A fusion approach using Dempster–Shafer theory and Bayesian learning

We propose a novel approach for credit card fraud detection, which combines evidences from current as well as past behavior. The fraud detection system (FDS) consists of four components, namely, rule-based filter, Dempster–Shafer adder, transaction history database and Bayesian learner. In the rule-based component, we determine the suspicion level of each incoming transaction based on the extent of its deviation from good pattern. Dempster–Shafer’s theory is used to combine multiple such evidences and an initial belief is computed. The transaction is classified as normal, abnormal or suspicious depending on this initial belief. Once a transaction is found to be suspicious, belief is further strengthened or weakened according to its similarity with fraudulent or genuine transaction history using Bayesian learning. Extensive simulation with stochastic models shows that fusion of different evidences has a very high positive impact on the performance of a credit card fraud detection system as compared to other methods.     

基于BP神经网络的医保欺诈识别

医疗保险欺诈是指在参加医保的过程中,通过故意捏造、虚构事实等方法骗取医保基金或医保待遇,造成医疗保险基金损失的行为.有效地识别医保欺诈对医保基金的健康使用有重大意义.本文运用BP神经网络实现医保欺诈的主动识别,并利用Logistic回归分析对神经网络模型进行改进,降低弱因子对神经网络识别的干扰.此外,应对欺诈数据的稀缺问题,采用只取正常数据训练神经网络模拟函数曲线的模式.实证表明,该方法对医保欺诈具有较好的识别能力.     

基于链上数据的区块链欺诈账户检测研究

针对区块链上存在的欺诈账户给交易带来的安全问题,提出了基于机器学习的欺诈账户的检测及特征分析模型,将以太坊上真实的链上数据进行特征提取后作为模型的数据来源,通过对不同的机器学习方法进行比较得到最优模型并进行迭代训练以获得最佳的预测模型,同时引入SHAP值对数据特征进行分析。实验结果表明,基于XGBoost的欺诈账户检测模型在RMSE、MAE和R²三组指标上达到了0.205、0.084和0.833,优于其余的对比模型,并结合SHAP值识别出预测欺诈账户的关键因素,为区块链的交易安全提供决策参考。     

A cost-sensitive decision tree approach for fraud detection

With the developments in the information technology, fraud is spreading all over the world, resulting in huge financial losses. Though fraud prevention mechanisms such as CHIP&PIN are developed for credit card systems, these mechanisms do not prevent the most common fraud types such as fraudulent credit card usages over virtual POS (Point Of Sale) terminals or mail orders so called online credit card fraud. As a result, fraud detection becomes the essential tool and probably the best way to stop such fraud types. In this study, a new cost-sensitive decision tree approach which minimizes the sum of misclassification costs while selecting the splitting attribute at each non-terminal node is developed and the performance of this approach is compared with the well-known traditional classification models on a real world credit card data set. In this approach, misclassification costs are taken as varying. The results show that this cost-sensitive decision tree algorithm outperforms the existing well-known methods on the given problem set with respect to the well-known performance metrics such as accuracy and true positive rate, but also a newly defined cost-sensitive metric specific to credit card fraud detection domain. Accordingly, financial losses due to fraudulent transactions can be decreased more by the implementation of this approach in fraud detection systems.     

Employing transaction aggregation strategy to detect credit card fraud

Credit card fraud costs consumers and the financial industry billions of dollars annually. However, there is a dearth of published literature on credit card fraud detection. In this study we employed transaction aggregation strategy to detect credit card fraud. We aggregated transactions to capture consumer buying behavior prior to each transaction and used these aggregations for model estimation to identify fraudulent transactions. We use real-life data of credit card transactions from an international credit card operation for transaction aggregation and model estimation.     

Adaptive Fraud Detection

One method for detecting fraud is to check for suspicious changes in user behavior. This paper describes the automatic design of user profiling methods for the purpose of fraud detection, using a series of data mining techniques. Specifically, we use a rule-learning program to uncover indicators of fraudulent behavior from a large database of customer transactions. Then the indicators are used to create a set of monitors, which profile legitimate customer behavior and indicate anomalies. Finally, the outputs of the monitors are used as features in a system that learns to combine evidence to generate high-confidence alarms. The system has been applied to the problem of detecting cellular cloning fraud based on a database of call records. Experiments indicate that this automatic approach performs better than hand-crafted methods for detecting fraud. Furthermore, this approach can adapt to the changing conditions typical of fraud detection environments.     

A novel two-stage phased modeling framework for early fraud detection in online auctions

Reported dollar losses from online auction fraud were over $43M in 2008 in the US (NW3C, 2009). In general, reputation systems provided by online auction sites are the most common countermeasure available for buyers to evaluate a seller’s credit. Unfortunately, feedback score mechanisms are too easily manipulated, creating falsely overrated reputations. In addition, existing research on online auction fraud shows that a more complicated reputation management system could weaken the motivation of committing a fraud. However, very few of the previous work addresses the most important issue of a fraud detection mechanism is to discover a fraudster before he defrauds as early as possible. Therefore, developing an effective early fraud detection mechanism is necessary to prevent fraud for online auction participants.This paper proposes a novel two-stage phased modeling framework that integrates hybrid-phased models with a successive filtering procedure to identify latent fraudsters by examining the phased features of potential fraudsters’ lifecycles. This framework improves the performance of identifying latent fraudsters disguising as legitimate accounts with diverse features. In addition, a composite of measuring attributes we devised in this study is also helpful in modeling fraudulent behavior. To demonstrate the effectiveness of the proposed methods, real transaction data were collected from Yahoo!Taiwan (http://tw.bid.yahoo.com/) for training and testing. The experimental results show that the true positive rate of detecting fraudsters is over 93% on average. Furthermore, the proposed framework can significantly improve the precision and the success rate of fraud detection; the experimental results also show that the fraud detection models constructed by conventional methods are ineffective in detecting latent fraudsters.     

Detecting fraudulent labeling of rice samples using computer vision and fuzzy knowledge

Pakistan’s climate allows growing several types of crops, among them is rice. Basmati is one of the most harvested and most profitable varieties of rice because of its unique fragrance. Rice varieties are difficult to differentiate accurately by visual inspection. Therefore, dishonest dealers could easily mislabel or adulterate basmati rice with less valuable assortments that look similar. We need a way to guard the interests of our trade partners. Many different approaches have been proposed to detect adulteration or fraud labeling of rice, in particular, to detect mixtures of authentic basmati and non-basmati varieties. These techniques employ characteristics such as morphological parameters, physicochemical properties, DNA, protein, or metabolites and are expensive and time-consuming. In this paper, we propose a novel and inexpensive technique to detect fraudulent labeling. We use computer vision and a fuzzy classification database for detecting fault labels. For classification, we employ a neural network based approach, and for detecting fraudulent labels, we create a fuzzy classification knowledge database to label rice samples accurately. Our proposed approach is novel and achieves a precision of more than 90% (for 10 gram sample) in identifying fraudulent labels of rice. We conclude that our approach can help in identifying the rice varieties with a higher accuracy.     

基于扩展主题模型的异常医疗处方检测方法

异常处方指的是医生为患者所开具的存在异常的处方。医疗处方中出现异常,如滥用药或者开错药等,会影响患者的治疗效率,甚至造成严重的后果。由于一些主观或者客观原因,医生总会开具一些异常处方。检测出这些异常处方能够提升患者就医效率,减少社会医疗成本,并且对药物滥用、多开药、错开药的有效管理等都有着重要意义。为此,提出了一种基于扩展主题模型的异常处方检测方法。该方法能够自动地从大量处方数据中检测出异常处方,并且对于每一个新的处方,该方法都能够判断其诊断和用药是否匹配,进而判断其是否正常。与其他异常检测算法相比,该方法具有更广泛的应用,不仅可以在医疗领域中使用,以检测异常处方,还可以在其他领域中使用,以检测其他特征之间的匹配关系异常。该方法已经得到了实现,并在真实的处方数据集中得到了验证。     

Identifying the signs of fraudulent accounts using data mining techniques

In today’s technological society there are various new means to commit fraud due to the advancement of media and communication networks. One typical fraud is the ATM phone scams. The commonality of ATM phone scams is basically to attract victims to use financial institutions or ATMs to transfer their money into fraudulent accounts. Regardless of the types of fraud used, fraudsters can only collect victims’ money through fraudulent accounts. Therefore, it is very important to identify the signs of such fraudulent accounts and to detect fraudulent accounts based on these signs, in order to reduce victims’ losses. This study applied Bayesian Classification and Association Rule to identify the signs of fraudulent accounts and the patterns of fraudulent transactions. Detection rules were developed based on the identified signs and applied to the design of a fraudulent account detection system. Empirical verification supported that this fraudulent account detection system can successfully identify fraudulent accounts in early stages and is able to provide reference for financial institutions.     

A Novel Strategy for Mining Highly Imbalanced Data in Credit Card Transactions

The design of an efficient credit card fraud detection technique is, however, particularly challenging, due to the most striking characteristics which are; imbalancedness and non-stationary environment of the data. These issues in credit card datasets limit the machine learning algorithm to show a good performance in detecting the frauds. The research in the area of credit card fraud detection focused on detection the fraudulent transaction by analysis of normality and abnormality concepts. Balancing strategy which is designed in this paper can facilitate classification and retrieval problems in this domain. In this paper, we consider the classification problem in supervised learning scenario by creating a contrast vector for each customer based on its historical behaviors. The performance evaluation of proposed model is made possible by a real credit card data-set provided by FICO, and it is found that the proposed model has significant performance than other state-of-the-art classifiers.     

Informing the development of a fraud prevention toolset through a situated analysis of fraud investigation expertise

Insurance fraud is a growing problem. This article describes a project that aimed to specify and develop a suite of computer-based tools to support the early detection and subsequent investigation of potentially fraudulent claims. System requirements were informed by ethnographic studies focusing on: (1) understanding current fraud detection practices and (2) characterising fraud investigation expertise. Tools were designed that sift claims for potential problem cases and assist in the processes of investigation and detection of new fraud types by providing anomaly capture, argumentation and visualisation environments. The resulting tools capitalise upon expertise and embody processes that can subsequently be used by inexperienced claims handlers to detect and deal with fraudulent claims.     

Feature Extraction Method Based on Social Network Analysis

Due to rapid development of Internet technology and electronic business, fraudulent activities have increased. One of the ways to cope with damages of them is fraud detection. In this field, there is a need for methods accurate and fast. Therefore, a novel and efficient feature extraction method based on social network analysis called FEMBSNA is proposed for fraud detection in banking accounts. In this method, in order to increase accuracy and control runtime in the first step, features based on network level are considered using social network analysis and extracted feature is combined with other features based on user level in the next phase. To evaluate our feature extraction method, we use PCK-means method as a basic method to learn. The results show using the proposed feature extraction as a pre-processing step in fraud detection improves the accuracy remarkably while it controls runtime in comparison with other methods.     

Chip and PIN – biggest UK retail project since decimalisation,but not enough on its own to defeat card fraud

The credit card industry's big idea for tackling fraud, chip and PIN, is being rolled out in earnest within the UK, but security experts warn that on its own it will merely divert criminals to other channels. Chip and PIN does nothing to address cardholder not present (CNP) fraud, notably for online purchases over the Internet, and still leaves the door open for fraudulent transactions from identify theft.     

点击欺诈群体检测与发现*

在线广告中的欺诈点击(click fraud)是指所有利用欺诈性手段或带有欺诈意图并被搜索引擎承认的点击行为。传统点击欺诈检测主要集中在检测个体用户点击的合法性。然而,目前存在很多的发布商雇佣大批网络用户,以群体形式进行欺诈点击。针对这一问题,提出了一种检测点击欺诈群组的方法。首先使用频繁项集挖掘算法来发现共同点击过大量广告的个体用户,作为疑似欺诈组。然后,在对组内用户点击行为属性分析的基础上,运用孤立点检测方法找到与组内其它用户有显著差异的疑似欺诈用户。最后,运用贝叶斯分类方法对检测到的所有疑似欺诈成员分类,得到真正的欺诈群组和欺诈用户。在真实数据集上的实验结果证明了方法的可行性与有效性。     

A survey of signature based methods for financial fraud detection

Fraud detection mechanisms support the successful identification of fraudulent system transactions performed through security flaws within deployed technology frameworks while maintaining optimal levels of service delivery and a minimal numbers of false alarms. Knowledge discovery techniques have been widely applied in fraud detection for data analysis and training of supervised learning algorithms to support the extraction of fraudulent account behaviour within static data sets. Escalating costs associated with fraud however have continued to drive the migration towards increasingly proactive methods of fraud detection, to support the real-time screening of transactional data and detection of ambiguous user behaviour prior to transaction completion. This shift in data processing from post to pre data storage significantly reduces the available time within which to evaluate newly arriving system requests and produce an accurate fraud decision, demanding increasingly robust and intelligent user profiling technologies to support advanced fraud detection. This paper provides a comprehensive survey of existing research into account signatures, an innovative account profiling technology which maintains a statistical representation of normal account usage for rapid recalculation in real-time. Fraud detection architectures, processing models and applications to date are critically examined and evaluated with respect to their proactive capabilities for detection of fraud within streaming financial data. Discussion is also presented on challenges which remain within the proactive profiling of account behaviour and future research directions within the signature domain.     

基于图卷积神经网络的医保欺诈检测算法

针对医疗保险欺诈检测当中欺诈样本不足、数据标注昂贵和传统基于欧氏空间的模型准确率低的问题,提出了一种新的基于图卷积和变分自编码的单分类医保欺诈检测模型(OCGVAE)。首先,通过病人就诊记录建立社交网络,计算病人和医生之间的权重关系,并设计了一个2层的图卷积神经网络(GCN)作为社交网络数据的输入,用以降低社交网络的数据维度;然后,设计了一个变分自编码(VAE)用以实现只存在一类欺诈样本标签的情况下的模型训练;最后,设计了一个逻辑回归(LR)模型用以判别数据类别。实验结果表明,OCGVAE模型的检测准确率达到87.26%,相较于一类对抗神经网络(OCAN)、一类高斯过程(OCGP)、一类近邻(OCNN)、一类支持向量机(OCSVM)和半监督图卷积神经网络(Semi-GCN)算法,分别高出16.1%、70.2%、31.7%、36.5%和27.6%,说明所提模型有效提高了医保欺诈筛查精度。     

A comparison of fraud cues and classification methods for fake escrow website detection

The ability to automatically detect fraudulent escrow websites is important in order to alleviate online auction fraud. Despite research on related topics, such as web spam and spoof site detection, fake escrow website categorization has received little attention. The authentic appearance of fake escrow websites makes it difficult for Internet users to differentiate legitimate sites from phonies; making systems for detecting such websites an important endeavor. In this study we evaluated the effectiveness of various features and techniques for detecting fake escrow websites. Our analysis included a rich set of fraud cues extracted from web page text, image, and link information. We also compared several machine learning algorithms, including support vector machines, neural networks, decision trees, naïve bayes, and principal component analysis. Experiments were conducted to assess the proposed fraud cues and techniques on a test bed encompassing nearly 90,000 web pages derived from 410 legitimate and fake escrow websites. The combination of an extended feature set and a support vector machines ensemble classifier enabled accuracies over 90 and 96% for page and site level classification, respectively, when differentiating fake pages from real ones. Deeper analysis revealed that an extended set of fraud cues is necessary due to the broad spectrum of tactics employed by fraudsters. The study confirms the feasibility of using automated methods for detecting fake escrow websites. The results may also be useful for informing existing online escrow fraud resources and communities of practice about the plethora of fraud cues pervasive in fake websites.     

CAT-RFE:点击欺诈的集成检测框架

点击欺诈是近年来最常见的网络犯罪手段之一,互联网广告行业每年都会因点击欺诈而遭受巨大损失。为了能够在海量点击中有效地检测欺诈点击,构建了多种充分结合广告点击与时间属性关系的特征,并提出了一种点击欺诈检测的集成学习框架——CAT-RFE集成学习框架。CAT-RFE集成学习框架包含3个部分:基分类器、递归特征消除(RFE,recursive feature elimination)和voting集成学习。其中,将适用于类别特征的梯度提升模型——CatBoost(categorical boosting)作为基分类器;RFE是基于贪心策略的特征选择方法,可在多组特征中选出较好的特征组合;Voting集成学习是采用投票的方式将多个基分类器的结果进行组合的学习方法。该框架通过CatBoost和RFE在特征空间中获取多组较优的特征组合,再在这些特征组合下的训练结果通过voting进行集成,获得集成的点击欺诈检测结果。该框架采用了相同的基分类器和集成学习方法,不仅克服了差异较大的分类器相互制约而导致集成结果不理想的问题,也克服了RFE在选择特征时容易陷入局部最优解的问题,具备更好的检测能力。在实际互联网点击欺诈数据集上的性能评估和对比实验结果显示,CAT-RFE集成学习框架的点击欺诈检测能力超过了CatBoost模型、CatBoost和RFE组合的模型以及其他机器学习模型,证明该框架具备良好的竞争力。该框架为互联网广告点击欺诈检测提供一种可行的解决方案。