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.

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.

Leveraging Readability and Sentiment in Spam Review Filtering Using Transformer Models

Online reviews significantly influence decision-making in many aspects of society.The integrity of internet evaluations is crucial for both consumers and vendors.This concern necessitates the development of effective fake review detection techniques.The goal of this study is to identify fraudulent text reviews.A comparison is made on shill reviews vs.genuine reviews over sentiment and readability features using semi-supervised language processing methods with a labeled and balanced Deceptive Opinion dataset.We analyze textual features accessible in internet reviews by merging sentiment mining approaches with readability.Overall,the research improves fake review screening by using various transformer models such as Bidirectional Encoder Representation from Transformers(BERT),Robustly Optimized BERT(Roberta),XLNET(Transformer-XL)and XLM-Roberta(Cross-lingual Language model–Roberta).This proposed research extracts and classifies features from product reviews to increase the effectiveness of review filtering.As evidenced by the investigation,the application of transformer models improves the performance of spam review filtering when related to existing machine learning and deep learning models.

A data reduction scheme for active authentication of legitimate smartphone owner using informative apps ranking

Smartphones are vulnerable to fraudulent use despite having strong authentication mechanisms.Active authentication based on behavioral biometrics is a solution to protect the privacy of data in smart devices.Machinelearning-based frameworks are effective for active authentication.However,the success of any machine-learningbased techniques depends highly on the relevancy of the data in hand for training.In addition,the training time should be very efficient.Keeping in view both issues,we’ve explored a novel fraudulent user detection method based solely on the app usage patterns of legitimate users.We hypothesized that every user has a unique pattern hidden in his/her usage of apps.Motivated by this observation,we’ve designed a way to obtain training data,which can be used by any machine learning model for effective authentication.To achieve better accuracy with reduced training time,we removed data instances related to any specific user from the training samples which did not contain any apps from the user-specific priority list.An information theoretic app ranking scheme was used to prepare a user-targeted apps priority list.Predictability of each instance related to a candidate app was calculated by using a knockout approach.Finally,a weighted rank was calculated for each app specific to every user.Instances with low ranked apps were removed to derive the reduced training set.Two datasets as well as seven classifiers for experimentation revealed that our reduced training data significantly lowered the prediction error rates in the context of classifying the legitimate user of a smartphone.

Analytical Methods in Quality Control of Scientific Publications Part IV: Fraud Ordered by the Pharmaceutical Industry

In a series of papers under the common title: “Analytical Methods in Quality Control of Scientific Publications,” cases of undisputable breaches of publication ethics and breaches of acceptable rules in the publication of scientific information have been presented. Clear cases of fraud, falsification by some authors, and unqualified review of papers by reviewers and editors were presented in: Analytical Methods in Quality Control of Scientific Publications, (2012) American Journal of Analytical Chemistry, Vol. 3, No. 6, pp. 443-447 DOI:10.4236/ajac.2012.36058. The place of authors, reviewers, editors, and publisher was discussed in: Editorial: Analytical Methods in Quality Control of Scientific Publications Part II: The Authors’, Reviewers’, Editors’ Responsibility, and the Publishers’ Authority, (2013) International Journal of Analytical Mass Spectrometry and Chromatography, Vol. 1, No. 2, pp. 81-89 http://dx.doi.org/10.4236/ijamsc.2013.12010, Analytical Methods in Quality Control of Scientific Publications Part III: Publishers’ Ethics and Editors’ Com- plicity, (2014) International Journal of Analytical Mass Spectrometry and Chromatography, Vol. 2, No. 3, pp. 77-102. DOI: 10.4236/ijamsc.2014.23008 and in the book Historical Overview of Chromatography and Related Techniques in Analysis of Antimalarial Drug Primaquine (Editor, Ilia Brondz) Nova Science Publishers, Inc., (2011) ISSN 978-1-61761-944-1. Here, the corrupting influence of the pharmaceutical industry as a customer and employer of pseudoresearchers and corrupt editors and even to corrupt journal publishers for publication of fraudulent information and pseudoscientific data will be discussed by identifying the authors of pseudoscientific publications, the editors who gave the green light for the fraudulent publication, and the pharmaceutical companies involved. Documentation will be given to support the accusations of fabricated fraudulent “scientific” data, and the publication of such data without sufficient revision and sufficient background for publication of papers will be illustrated by discussing the content of papers: “Determination of Quinocide as Impurityin Primaquine Tablets by Capillary Zone Electrophoresis,” Abdalla A. Elbashir et al., (2009) Biomedical Chromatography, Vol. 23, pp. 464-471, published in Wiley Interscience, “Development of a Capillary Electrophoresis Method for the Enantioselective Estimation of Primaquine in Pharmaceutical Formulations,” Abdalla A. Elbashir et al., (2008) Journal of AOAC International, Vol. 91, No. 3, pp. 536-541, “Enantioselective Analysis of Primaquine and Its Impurity Quinocide by Capillary Electrophoresis,” Abdalla A. Elbashir et al., (2009) Biomedical Chromatography, Vol. 23, pp. 295-301, published in Wiley Interscience and other pseudoscientific publications by these teams of pseudoscientists.

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.

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.

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.

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.

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）.

Hacker 2.0?

More measures need to be taken to cope with fraudulent Internet practices Pinduoduo,a Chinese on line group discounter,announced on January 20,that someone had exploited a loophole on its platform to steal tens of millions of yuan worth of discount vouchers during its New Year’s promotion.Though the company immediately rectifi ed the bug,it still resulted in a loss of 10 million yuan($1.49 million),according to the company’s risk control team.

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 fraudsters for web search fraud.