Image Recognition Model of Fraudulent Websites Based on Image Leader Decision and Inception-V3 Transfer Learning

The fraudulent website image is a vital information carrier for telecom fraud.The efficient and precise recognition of fraudulent website images is critical to combating and dealing with fraudulent websites.Current research on image recognition of fraudulent websites is mainly carried out at the level of image feature extraction and similarity study,which have such disadvantages as difficulty in obtaining image data,insufficient image analysis,and single identification types.This study develops a model based on the entropy method for image leader decision and Inception-v3 transfer learning to address these disadvantages.The data processing part of the model uses a breadth search crawler to capture the image data.Then,the information in the images is evaluated with the entropy method,image weights are assigned,and the image leader is selected.In model training and prediction,the transfer learning of the Inception-v3 model is introduced into image recognition of fraudulent websites.Using selected image leaders to train the model,multiple types of fraudulent websites are identified with high accuracy.The experiment proves that this model has a superior accuracy in recognizing images on fraudulent websites compared to other current models.

DC-FIPD: Fraudulent IP Identification Method Based on Homology Detection

Currently,telecom fraud is expanding from the traditional telephone network to the Internet,and identifying fraudulent IPs is of great significance for reducing Internet telecom fraud and protecting consumer rights.However,existing telecom fraud identification methods based on blacklists,reputation,content and behavioral characteristics have good identification performance in the telephone network,but it is difficult to apply to the Internet where IP(Internet Protocol)addresses change dynamically.To address this issue,we propose a fraudulent IP identification method based on homology detection and DBSCAN(Density-Based Spatial Clustering of Applications with Noise)clustering(DC-FIPD).First,we analyze the aggregation of fraudulent IP geographies and the homology of IP addresses.Next,the collected fraudulent IPs are clustered geographically to obtain the regional distribution of fraudulent IPs.Then,we constructed the fraudulent IP feature set,used the genetic optimization algorithm to determine the weights of the fraudulent IP features,and designed the calculation method of the IP risk value to give the risk value threshold of the fraudulent IP.Finally,the risk value of the target IP is calculated and the IP is identified based on the risk value threshold.Experimental results on a real-world telecom fraud detection dataset show that the DC-FIPD method achieves an average identification accuracy of 86.64%for fraudulent IPs.Additionally,the method records a precision of 86.08%,a recall of 45.24%,and an F1-score of 59.31%,offering a comprehensive evaluation of its performance in fraud detection.These results highlight the DC-FIPD method’s effectiveness in addressing the challenges of fraudulent IP identification.

Multimodal Fraudulent Website Identification Method Based on Heterogeneous Model Ensemble

The feature analysis of fraudulent websites is of great significance to the combat,prevention and control of telecom fraud crimes.Aiming to address the shortcomings of existing analytical approaches,i.e.single dimension and venerability to anti-reconnaissance,this paper adopts the Stacking,the ensemble learning algorithm,combines multiple modalities such as text,image and URL,and proposes a multimodal fraudulent website identification method by ensembling heterogeneous models.Crossvalidation is first used in the training of multiple largely different base classifiers that are strong in learning,such as BERT model,residual neural network(ResNet)and logistic regression model.Classification of the text,image and URL features are then performed respectively.The results of the base classifiers are taken as the input of the meta-classifier,and the output of which is eventually used as the final identification.The study indicates that the fusion method is more effective in identifying fraudulent websites than the single-modal method,and the recall is increased by at least 1%.In addition,the deployment of the algorithm to the real Internet environment shows the improvement of the identification accuracy by at least 1.9%compared with other fusion methods.

The Effects of Audit Committee Attributes on Fraudulent Financial Reporting

This study aims to examine the relationship between audit committee attributes （audit committee independence, financial expertise, meeting frequency, gender diversity, and ethnic composition） and the propensity for fraudulent financial reporting. The sample includes 116 fraudulent and non-frandulent firms listed on Bursa Malaysia from 2005 to 2010. The finding of this study indicates that audit committee independence is positively associated with fraudulent financial reporting. The higher the proportion of independent or outside directors on the committee, the higher the possibility of financial fraud, and vice versa. The results also show that the expertise of members of the audit committee is negatively associated with corporate fraud. This suggests that when audit committee members are financially literate, they are more competent to curb fraudulent financial reporting. However, the findings for frequency of audit committee meetings, gender, and ethnicity show that there is no relationship between these variables and corporate fraud. The result of this study is robust after controlling for other firm-specific effects.

Forensic Auditing Techniques and Fraudulent Practices of Public Institutions in Nigeria

Public institutions are charged with the responsibility of providing essential services for the welfare of the citizens by manipulating the economy＇s financial flow through public expenditure, taxation, and so on. The reliance on public institutions to provide public services in Nigeria has resulted in disappointing results, because chief executives of the institutions take less interest in the degree of its success, and this accounts for the high level of fraudulent practices in such institutions. This study, therefore, examined the relationship between forensic auditing and fraudulent practices in Nigerian public institutions. To achieve this purpose, some hypothetical statements were made and a review of relevant literature was explored. The population of the study consisted of the general managers and accountants of 12 public institutions in Nigeria. The data generated were statistically tested with the Pearson Product-Moment Correlation Coefficient. The findings suggest that both the proactive and reactive forensic auditing techniques have a negative significant relationship with fraudulent practices in Nigerian public institutions. Based on the above, it was recommended that： （1） The Economic and Financial Crime Commission （EFCC）, the Independent Corrupt Practices Commission （ICPC）, and other anti-corruption bodies in Nigeria should have, in their payroll, internal forensic auditors to supplement the duties of the internal auditors; （2） Forensic auditors should regularly undergo training and development programs to acquaint them with relevant knowledge and skills for effective forensic auditing; and （3） Forensic auditing should be made mandatory for public institutions by regulatory authorities rather than being voluntary.

Fraudulent Financial Reporting： Theory and Evidence

This paper develops a hypothesis of the four factors from the cause of fraudulent financial reporting in terms of thoughts, culture, motive, opportunity and economic trade-off. Then tests whether independent directors monitor and improve the quality of financial report on the basis of 1 170 firms in China by means of OLS regression. The evidence is consistent with the hypothesis.

Fraudulent phone call recognition method based on convolutional neural network

With increasingly rampant telephone fraud activities,the social impact and economic losses caused to China have increased dramatically.Precise,convenient,and efficient fraudulent phone call recognition has become a challenge since telephone fraud became more varied and covert.To deal with this problem,many researchers have extracted some statistical features of telephone fraud behavior and proposed some machine learning algorithms on the field of fraudulent phone call recognition.In this paper,the calling detail records are utilized to construct a classifier for fraudulent phone call recognition.Meantime,a deep learning approach based on convolutional neural network(CNN)is proposed for better features learning and compared with the existing state-of-the-art machine learning algorithms.It learns phone number and call behavior features of telephone fraud,and improves the accuracy of classification.The evaluation results show that the proposed algorithm outperforms competitive algorithms.

NEQM （NFC Based Equipment Qualification Management） System Preventing Counterfeit and Fraudulent Item

Qualification of equipment essential to safety in NPPs （nuclear power plants） ensures its capability to perform designated safety functions on demand under postulated service conditions. However, a number of incidents identified by the NRC （Nuclear Regulatory Commission） since 1980s catalysed the US nuclear industry to adopt standard precautions to guard against counterfeit items. The purpose of this paper is to suggest the NFC （near field communication） based equipment qualification management system preventing counterfeit and fraudulent items. The NEQM （NFC based equipment qualification management） system works with the support of legacy systems such as PMS （procurement management system） and FMS （facility management system）.

债权人保护视角下的离婚财产协议

离婚协议中有关财产及债务处理的条款存在诈害债权的情形时,可根据其性质参照适用或直接适用债权人撤销权的规定,且原则上应当属于无偿处分财产权益。相关条款无偿性的判断以法定的或司法裁判确定的财产分配比例为参考基准,当事人在条款中约定的分配比例显著超出合理基准,且实际影响债权人债权实现的,其超出部分构成可被撤销的诈害行为。债权人应当承担条款具有诈害性的举证责任,其行使撤销权的范围限于其债权和超出部分的数额,且应当在除斥期间内行使,以避免债权撤销权对离婚协议意思自治的过度介入。

关于虚开增值税专用发票罪之“虚开行为”再探讨

增值税发票犯罪案件数量庞多,严重损害了国家的财政收入和税收管理秩序。司法机关在认定虚开增值税专用发票罪的“虚开行为”过程中,存在着诸多争议。认定虚开行为是否需要具有偷税的目的,是否需要造成税收损失,是否还可以适用“三流一致”标准,实务中对此有不同观点。通过对现行法律法规、司法解释归纳总结,对“虚开行为”展开深入分析,提出了统一虚开行为认定标准、完善虚开行为刑法体系、加强司法与税务行政程序衔接等改革建议,以期充分廓清虚开增值税专用发票罪中的“虚开行为”。

冒用花呗行为定性之争:问题、本质及解释

互联网金融领域的刑事犯罪治理难题表现出消极的点面效应。围绕冒用花呗的行为定性,至少存在花呗的法律属性定位、机器能否被骗、机器如何被骗的分析难题。其一,对于花呗的法律属性,研究论证的瑕疵在于客观解释立场的缺失。根据客观解释立场,“其他金融机构”中的“其他”意表除了商业银行以外的可以发行信用卡的金融机构,花呗属于刑法意义上的信用卡。其二,关于机器能否被骗。机器不具有自我意识的认识桎梏不能说明机器不可以被骗,否则只会固化人机关系“二元认识论”的旧观念,故机器不能被骗的立场应当被摒弃。其三,关于机器如何被骗。在探讨人工智能作为诈骗对象时引入预设同意理论已成为学界共识,然而该理论的运用现状过于粗简,其不仅可以说明机器的处分意识来源,更能说明人机关系的一体化。冒用花呗的行为定性中,关键特征是“人机交互的一体关系”,机器是自然人的电子代理人,人所排斥之事项即为机器所排斥之事项。第三方支付对于冒用者的身份要素陷入了错误认识,进而导致被害人财产受损。冒用花呗的行为应当定性为信用卡诈骗罪。

传销范围的重新界定及查处职权配置的优化——基于《禁止传销条例》修改的思考

传销作为舶来品在我国历经了允许、限制到禁止的演进历程。面对当下传销与直销的纠缠及其多层级直销、不同类型分销经营模式的复杂,尤其是数字经济下新型网络微商的出现,修改《禁止传销条例》《直销管理条例》被立法部门提到日程。基于经济政策和社会结构的变化、发展新质生产力的要求,《禁止传销条例》修改有必要通过刑法及其相关规定、解释来重新厘定传销的边界范围,确保行政立法与刑事立法在实体和程序上的衔接,保持法秩序统一下对传销治理的市场化、法治化。在传销性质界定上,应限定其范围,保持与直销新营销模式的协调;在治理方式上,实行由刑事治罪与行政治理并重向倚重行政治理转变,赋予行政执法足够的治理措施,改变现有冻结等措施向司法机关申请的规定,赋予其紧急止付等新的行政措施;在职权配置上,对诈骗型传销、经营型传销以及网络传销等新类型优化公安机关和市场监督部门以及其他部门的查处职权,构建重罚传销组织、领导者和约束积极参加者的梯度性规制体系,保持刑法的谦抑性,推进协同治理传销有效机制的建设。

科学研究中的“善意”监督及其路径探究

科学共同体内部的监督对披露科研不端行为、加强学风建设有重要作用,但应在“善意”这一基本原则指导下进行。“善意”监督往往会因为主观偏见盛行和客观保障缺失而陷入困境。为实现“善意”监督,应通过科研伦理教育进行主观引导,并通过完善相关科研伦理规范、规范监督和举报程序、健全奖惩制度、建立救济制度等措施为“善意”监督提供客观保障。

基于语用分析的诈骗电话结构化模型构建

电话诈骗因类型多变而难以识别和防范,为解析诈骗电话话术,对诈骗电话进行语用分析和结构化描述。首先,明确诈骗电话的语用目标;之后,分析诈骗电话为达成语用目标需包含的要素,以及要素功能和实施方式;最后,根据分析结果构建诈骗电话的结构化模型,描述会话结构、模块功能、实施策略、模块内容等方面特征。基于言语行为适切条件,实现针对诈骗电话的结构化解析,为设计具有可解释性的诈骗电话自动识别算法提供理论基础。

基于CRIME理论的康美药业财务造假分析及防范研究

随着经济的快速发展,资本市场中财务造假事件频繁发生,损害了中小股东的利益,给债权人、广大投资者乃至社会带来了巨大的损失。利用传统财务指标对舞弊手段进行识别的作用有限,提出的解决措施治标不治本。因此,需要采用系统的理论对上市公司财务舞弊进行深入分析,才能提出针对性的治理对策。本文运用CRIME舞弊行为理论,从舞弊者、舞弊手段、舞弊动机、监管机制、舞弊后果五个角度,对康美药业舞弊现象进行剖析,针对产生的问题提出了相应的预防措施,以期增强投资者的风险辨识能力。

价值驱动的以太坊交易追踪排名方法

区块链技术的匿名性与价值传递特性可能被恶意攻击者利用以实施网络钓鱼或其他欺诈行为。虽然链上数据公开、透明、可追溯,但是攻击者仍可通过设计复杂的交易链路,使资产在众多账户之间进行流转。最终,这些资产可能会被集中至某交易所账户并被提取,从而实现非法的利益获取。针对上述问题,面向以太坊提出一种价值驱动的交易追踪排名方法。首先收集12起诈骗金额超过百万美元的以太坊攻击案例,获取大小为27 GB的交易数据,构建地址图;然后从链上抽取代币的流动池数据,计算代币历史价格,确定地址图中各交易的权重系数;最后提出基于价值占比的动态残差放缩机制,优化地址图结构,更加偏向主要的价值流通路径。实验结果表明,召回率可达89.24%,相较于交易追踪排名(transaction tracing rank,TTR)、APPR和Haircut算法分别提高了7%、20%和37%,验证了本文方法在检测欺诈账户上的高效性和准确性。

个人破产界限与虚假破产罪的重构研究

防范破产欺诈是诚信文化及个人破产制度的价值需求,但现行企业破产界限低效,加之破产程序自身属性与虚假破产罪犯罪构成存在诸多不适应之处,弱化二者功能的发挥。未来个人破产制度建构中,为高效防范破产欺诈行为,不能简单地进行改造,需沿适应性的路径,以保护“诚实而不幸”债务人作为破产界限与虚假破产罪的共同价值,并从维护债权人权益化解矛盾、提升破产界限效率价值发挥破产制度功能及尊重刑法谦抑性妥当配置虚假破产罪的犯罪构成,以实现制度利益的均衡。

基于多维特征的涉诈网站检测与分类技术研究

随着互联网的发展与普及,涉诈团伙诈骗手法与反检测技术愈发先进,涉诈网站的检测与分类对于网络空间安全重要性更加显著,而传统的检测技术已无法应对现在的新型诈骗网站,并且针对涉诈网站分类的研究很少.针对此热点难题,本文分析了当今新型涉诈网站的多个典型特征并提出了一种基于多维特征的涉诈网站检测与分类系统.该系统共构建11种涉诈网站特征与3600个网页关键词来表示一个涉诈网站.系统首先利用爬虫获取待检测域名的网页截图、WHOIS信息与源码并交给特征抽取模块构建多维特征集.检测模块提取网站域名、代码结构以及网站WHOIS信息作为特征,构建随机森林模型实现检测任务.然后基于检测结果,网页分类模块利用双向GRU提取网页的文本特征,在置信度小于0.7的情况下使用BERT模型从而保证系统准确度与效率,并使用残差神经网络提取网页截图特征,同时计算网页内部图片与网站Logo相似度,创建随机森林模型进行分类,并设计了对比实验进一步分析模型的准确性.实验证明,本文提出的模型拥有很高的准确性,模型平均F1-score达到97.28%.实验结果表明,本文提出的多维特征模型能很好地区分涉诈网站与正常网站,克服了传统方法应对新型涉诈网站的识别问题,并适用于全球新增域名的涉诈网站快速检测与分类.

专利交易中区块链应用的三方演化博弈分析

区块链技术有助于应对专利交易中信息不透明、确权难等问题,为专利交易模式的创新提供了新的选择。通过分析区块链在专利交易中的作用,构建由交易申请方、交易平台、意向受让方组成的三方演化博弈模型。基于博弈收益矩阵分析专利交易系统的演化稳定点,讨论主体的行为策略;并在此基础上,对不同情境进行初始参数赋值,通过仿真分析验证关键因素对专利交易系统及各参与主体行为策略的影响。研究结果表明:参数的数值关系会影响专利交易系统的演化稳定策略;交易平台的区块链应用策略主要受到运营成本、欺诈行为所带来损失的影响;区块链作用下掩饰成本的增加,能够抑制交易申请方的欺诈行为;调查成本与所遭受损失的降低,有助于促使意向受让方选择交易。在丰富相关理论研究的同时,为专利交易中区块链应用实践提供参考。

受欺诈婚姻的法律适用研究

我国有关受欺诈婚姻的独特立法构造致使受欺诈婚姻在法律适用中存在着解释论的难题。受欺诈婚姻从民法体系来看属于一般欺诈法律行为的特殊情形,从受欺诈婚姻理论、受欺诈婚姻的替代救济以及受欺诈婚姻的现实需求出发,受欺诈婚姻存在适用民事法律行为规范的空间。在适用法律行为的规范时,身份法基本原则体现出来的价值共识是解释婚姻实质的归依,意思表示自由与欺诈行为的实质影响是认定受欺诈婚姻的双重评价标准。受欺诈方配偶可以主张撤销婚姻,但欺诈行为对婚姻的实质影响消失后,受欺诈婚姻的效力得以补正。