基于神经网络和SNA的C2C电子商务信誉欺诈识别研究*

当前,互联网中信誉欺诈行为严重影响了C2C电子商务的发展。如何有效识别互联网上的信誉欺诈商户是当前的研究热点,文中阐述了将基于交易历史的社会网络分析用于构建C2C电子商务信誉欺诈识别指标体系的原理及过程,通过使用LVQ神经网络对雅虎奇摩拍卖网采集的大量用户交易数据进行分类,有效识别出控制多个虚假账户以达到信用累计目的的信誉欺诈商户,对维护C2C电子商务交易的稳定性具有重要意义。     

参与式感知系统中用户累积行为信誉计算

参与式感知系统中,由于感知数据质量可能受参与者影响,提出了基于用户累积行为的信誉计算模型以帮助选择可信赖用户.针对感知环境中用户群体的广泛性及核心用户的不确定性,该模型采用OPTICS聚类算法定义用户场景并划分行为数据集,建立用户累积行为信誉计算模型,同时引入时间戳标记信息抛弃部分旧行为以更新用户信誉.实验表明,该信誉模型能够结合新旧行为较好地计算并调整用户信誉,在感知环境用户信誉评价中具有良好的应用前景.     

基于C2C的可信信用评价模型

提出一个基于C2C的可信信用评价模型。该模型根据历史交易记录,在综合考虑交易金额、买卖双方信誉度、交易次数、差评次数、未评价交易的基础上,采用动态计算的方式,为交易的成功进行提供可靠的依据。该模型可以有效甄别恶意用户和诚信用户,从而减少信誉诋毁行为,并可减少信誉榨取行为,提高C2C电子交易的安全性。     

在P2P网络中基于簇的全局信誉模型的仿真

P2P网络中存在着大量自私行为和欺诈行为,而现有的信任模型无法真实有效地衡量节点的可信度.针对上述问题,提供了一种快速、安全、优质的信任模型.将簇引入信誉模型制定了一系列管理规则,并将多维度引入信誉算法.各节点遵循管理规则自发形成三种不同的簇域,簇域等级越高,簇内节点服务和被服务的机会和优先权就越高.使用信任度、网络贡献度、累计滥用信任3个参数来计算节点的全局信誉.通过进行仿真和实验分析,验证了信誉模型的合理性和对恶意行为的防御能力.     

基于KM-SVMSMOTE-CNN的信用卡欺诈检测

针对信用卡欺诈检测中样本数据规模大, 计算复杂程度高, 数据分布极度不平衡等问题, 提出卷积神经网络(CNN)结合大规模信用卡交易数据进行欺诈检测, 同时为了解决交易数据的极端不平衡性问题, 使用K-means算法进行聚类, 结合支持向量机合成少数类过采样技术(SVMSMOTE)增加少数类样本数量, 最终构建一个KM-SVMSMOTE-CNN的信用卡交易欺诈预测模型. 选取Kaggle平台上发布的信用卡欺诈数据进行验证, 实验结果表明, 基于KM-SVMSMOTE-CNN的融合模型从整体上大大提高了信用卡欺诈检测的识别率.     

基于社会网络与信誉的C2C信任评价模型

将概率论和社会网络理论应用到信任传递以及多信任路径建模中,针对C2C交易平台实际的商业网络特征,提出基于社会网络与信誉的C2C信任评价模型。引入相似度计算建立簇与簇之间的关联,以解决C2C交易网络存在的买卖分工明确、稀疏性、聚簇问题。对淘宝网C2C交易平台数据的分析表明,该模型对淘宝网现有信誉机制具有较好的改进和补充作用,更符合现实生活中人与人之间的信任和推荐关系。     

基于博弈论抗共谋攻击的全局随机化共识算法

随着区块链技术的不断发展,作为区块链技术基石的共识技术受到更多关注,共识技术的发展越发迅速,但依旧存在相关难题。容错类共识算法作为区块链共识技术的代表性之一,依然存在诸多难题待研究,针对容错类共识算法中节点随机性和节点共谋攻击问题进行了研究,提出基于博弈论抗共谋攻击的全局随机化共识算法,通过实现节点的随机化和解决相关安全问题提高区块链网络的安全性和吞吐量。在选择参与容错类共识算法的节点过程中,利用映射函数和加权随机函数实现发起者和验证者节点的全局随机化,从而保证发起者和验证者节点的身份匿名,提高区块链网络的安全性。利用信誉更新模型实现信誉动态更新的同时利用博弈论分析容错类共识算法的安全问题,构造更加正确和高效的算法模型以提高算法的吞吐量并分析发现这类算法中存在超过1/3节点的共谋攻击问题,利用精炼贝叶斯博弈构造共谋合约,分析求得共谋者之间的纳什均衡点,从而解决超过1/3节点的共谋攻击问题。通过安全性分析和实验表明,基于博弈论抗共谋攻击的全局随机化共识算法相对工作量证明（PoW,proof of work）、权益证明（PoS,proof of stake）和实用拜占庭容错（PBFT,practical Byzantine fault tolerance）共识算法不仅提高吞吐量、降低计算资源消耗,而且该算法抵抗分布式拒绝服务（DDoS,distributed denial of service）、Eclipse attacks和超过1/3节点共谋攻击。     

基于熵权的群组动态优化竞争的P2P信任模型

针对P2P网络中节点行为的动态性改变和共谋攻击等关键问题,提出了一种基于熵权的群组动态优化竞争的P2P信任模型。该模型以节点全局信任度为依据,在逻辑上将节点分至不同速率群组;基于"熵权法"计算群组熵值及权重值,并更新时间周期内群组节点局部信任度,计算群组局部评价信誉标准差和节点局部评价标准差;采用动态优化竞争策略,调整节点或群组。该模型不仅在节点分组的基础上引入了群组权重值,以提升信任计算的准确度,而且在优化竞争过程中考虑节点行为的相似性,以强化激励策略。仿真实验表明,该模型在识别、抑制节点的动态性振荡行为和抑制共谋攻击行为方面均有一定的改进,在交易成功率方面有一定的提高。     

一种三段式高校IC卡反欺诈模型

针对高校IC卡的伪造、复制、篡改等欺诈行为,设计了一种“三段式”反欺诈流程,通过对IC卡的交易数据进行检查计算,筛选出存在异常状态的卡,在此基础上结合数据挖掘,建立了反欺诈模型。经过实验确认,该模型在检测针对高校IC卡的欺诈行为方面具有较好的可行性和有效性。     

一种高效的集中信任模型下的共谋检测算法

在各种信任管理模型中,共谋欺骗是最棘手的恶意行为之一. 由于信任值的主观特性,目前对共谋欺骗还没有较好的判定方法. 本文利用聚类方法,提出一种集中信任模型下的共谋检测算法. 为检验算法性能和参数设置,文中设计了相关的仿真实验,并进行了量化分析. 实验结果表明,在选取合适参数的情况下,算法具有较好的检测效果.     

基于群组的C2C电子商务信任模型研究

国内的电子商务平台大多采用eBay信任模型,对买家反馈评分进行简单累加得到卖家信誉值,未区分买家反馈评分的合理性及其参考价值的重要程度。为此,提出一种基于群组的C2C电子商务信任模型,通过计算买家与卖家的熟悉度,确定买家的可信度,综合考虑反馈评分、交易次数、交易价格、交易时间、以往买家的可信度对信誉的影响,构建电子商务信任模型。模拟实验结果证明,相比Sporas模型,该模型能为买家提供更全面、更准确的卖家信誉信息。     

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.     

Countering the collusion attack with a multidimensional decentralized trust and reputation model in disconnected MANETs

The FIRE trust and reputation model is a de-centralized trust model that can be applied for trust management in unstructured Peer-to-Peer (P2P) overlays. The FIRE model does not, however, consider malicious activity and possible collusive behavior in nodes of network and it is therefore susceptible to collusion attacks. This investigation reveals that FIRE is vulnerable to lying and cheating attacks and presents a trust management approach to detect collusion in direct and witness interactions among nodes based on colluding node’s history of interactions. A witness ratings based graph building approach is utilized to determine possibly collusive behavior among nodes. Furthermore, various interaction policies are defined to detect and prevent collaborative behavior in colluding nodes. Finally a multidimensional trust model FIRE+ is devised for avoiding collusion attacks in direct and witness based interactions. The credibility of the proposed trust management scheme as an enhancement of the FIRE trust model is verified by extensive simulation experiments.     

对抗共谋及节点行为动态性的P2P信任模型

针对现有P2P信任模型存在的网络开销大、不能很好地处理节点行为的动态性改变及共谋攻击等问题,提出了一种新的信任模型。通过直接交互节点的局部评价加权其评价可信度计算节点的全局信誉值;采用基于局部评价标准差、局部评价集中度的方法识别和抑制共谋攻击;根据节点行为的改变动态更新其信誉值和评价可信度。仿真实验表明,提出的模型较现有模型在网络开销、抑制共谋攻击和节点行为的动态性等方面有较大改进。     

On Desideratum for B2C E-Commerce Reputation Systems

This paper reviews existing approaches to reputation systems, their constraints as well as available solutions. Furthermore, it presents and evaluates a novel and comprehensive reputation model devoted to the distributed reputation system for Business-to-Consumer (B2C) E-commerce applications that overcomes the discussed drawbacks. The algorithm offers a comprehensive approach as it considers a number of issues that have a bearing on trust and reputation such as age of ratings, transaction value, credibility of referees, number of malicious incidents, collusion and unfair ratings. Moreover, it also extends the existing frameworks based on information about past behaviour, with other aspects affecting online trading decisions which relate to the characteristic of the providers, such as existence of trustmark seals, payment intermediaries, privacy statements, security/privacy strategies, purchase protection/insurance, alternative dispute resolutions as well as the existence of first party information.     

The design of FFML: A rule-based policy modelling language for proactive fraud management in financial data streams

Developing fraud management policies and fraud detection systems is a vital capability for financial institutions towards minimising the effect of fraud upon customer service delivery, bottom line financial losses and the adverse impact on the organisation’s brand image reputation. Rapidly changing attacks in real-time financial service platforms continue to demonstrate fraudster’s ability to actively re-engineer their methods in response to ad hoc security protocol deployments, and highlights the distinct gap between the speed of transaction execution within streaming financial data and corresponding fraud technology frameworks that safeguard the platform. This paper presents the design of FFML, a rule-based policy modelling language and encompassing architecture for facilitating the conceptual level expression and implementation of proactive fraud controls within multi-channel financial service platforms. It is demonstrated how a domain specific language can be used to abstract the financial platform into a data stream based information model to reduce policy modelling complexity and deployment latencies through an innovative policy mapping language usable by both expert and non-expert users. FFML is part of a comprehensive suite of assistive tools and knowledge-based systems developed to support fraud analysts’ daily work of designing new high level fraud management policies, mapping into executable code of the underpinning application programming interface and deployment of active monitoring and compliance functionality within the financial platform.     

MANET中基于健壮声誉机制的组播路由算法

提出了一种基于阶跃度的健壮声誉机制。该机制成功消除了由于节点勾结引起的声誉值误判，提高了声誉机制的健壮性。且在进行声誉值计算时，综合了历史因素。并将该机制应用于组播路由中，提出了一种基于声誉机制的概率组播路由算法RBPMRP。实验证明，在网络自私节点增多的情况下，该算法能显著提高包的平均传输率。     

面向价值的O2O电子商务信任预测模型

电子商务市场所面临的信用欺诈制约了O2O电子商务的快速发展。针对电子商务交易各方利益不平衡的问题,设计了一种面向价值的O2O电子商务信任预测模型。在该模型中,综合考虑了交易评价、交易时间、交易金额、交易次数、已有声誉值、评价的可信度等多种影响因素,利用置信因子对局部声誉和全局声誉进行整合,预测线下商家和消费者的信任度。实验证明,该信任模型能够有效计算线下商家和消费者的动态信任度,并对不诚信交易行为具有较强的防御能力,可实时更新O2O电子商务交易各方的信任度。     

基于蚁群算法的加强型可抵御攻击信任管理模型

通过将网络节点推荐行为分析和网络恶意节点密度的自适应机制纳入信誉度评价过程,提出了基于蚁群算法的加强型可抵御攻击信任管理模型--EAraTRM,以解决传统信任模型因较少考虑节点的推荐欺骗行为而导致容易在恶意节点的合谋攻击影响下失准的问题。在对比研究中发现,EAraTRM可以在网络中恶意节点密度达到90%,其他传统信任模型已经失效的情况下,仍保持较高的正确性。实验结果表明,EAraTRM能提高节点评价其他节点信誉度时的精度,并降低整个网络中恶意节点间进行合谋攻击的成功率。