Downloads & Free Reading Options - Results

http://media.ccc.de/browse/congress/2014/31c3_-_6120_-_en_-_saal_1_-_201412271600_-_practical_emv_pin_interception_and_fraud_detection_-_andrea_barisani.html This talks follows our previous EMV research uncovering new findings as well as a detailed analysis of Chip & PIN fraud markers in order to benefit cardholders, as well as issuing banks, in preventing wrongful liability for fraudulent charges. Andrea Barisani Help us caption & translate this video! http://amara.org/v/FuHR/ Source: https://www.youtube.com/watch?v=_Ai-3NG2UVA Uploader: media.ccc.de

Android mobile applications become an simple target for the attacker because of its open source background. Also user' lack of knowledge the process of installing and usage of the apps. To categorize fake and malware apps, all the earlier methods listening carefully on getting permission from the user and executing that particular mobile apps. A malware detection structure that discover fraudulent developer, to detect search rank fraud as well as malware in Google Play Store. The fraud application is detected by aggregate the three pieces of proof such as ranking based, co review based and rating based evidence. It combine efficiently for all the evidence for fraud detection. Detect fraud ranking in daily Apps head board. Avoid ranking manipulation. In the proposed system the detecting of normal and harmful application is analyzed by the SVM Algorithm. This system will analyze the uploaded application that are to be classifying the status which is dangerous application or normal application. The client can view the both the normal and harmful apps in ASP.NET. They can download the application after screening the secret manner. After using the apps the client can give the review on that downloaded apps. By the known review post for any application the admin will analyze the ASP.NET application for giving the ranking. The reviews are analyzed by the SVM Algorithm. V. Booma | G. Gayathri "Fraud Malware Detection in Google Play Store" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-4 , June 2020, URL: https://www.ijtsrd.com/papers/ijtsrd31686.pdf Paper Url :https://www.ijtsrd.com/computer-science/computer-security/31686/fraud-malware-detection-in-google-play-store/v-booma

Perma.cc archive of https://www.sciencedirect.com/science/article/pii/S105905602100143X?casa_token=EoNv01fsyDEAAAAA:aV0ttquA8wA7OozIfVTDpBku0ITg67G7AaYywxcHPxB9r6oE8NXHMkOEE2RYn6fMirq0aR_CQw created on 2021-12-16 18:20:44+00:00.

Browser fingerprinting and user behavior tracking are powerful techniques used by most fraud and bot detection solutions. These are implemented via JavaScript snippets running in the user browser. In this presentation, we’ll demystify the signals these snippets collect and describe why these signals are unreliable. Using a realistic threat model, we’ll describe various attacks against defenses relying on these signals. Finally, we'll share war stories of architectural and implementation flaws we found in real world deployments. Speaker Mayank Dhiman Security Engineer, Dropbox Mayank is a security researcher with an extensive background in network security, fraud/abuse prevention, threat intelligence and authentication. His research on browser fingerprinting and passive network fingerprinting has helped build rule-based systems and ML models to tackle fraud. - Managed by the official OWASP Media Project https://www.owasp.org/index.php/OWASP_Media_Project Source: https://www.youtube.com/watch?v=odPZ3z-yz1k Uploader: OWASP

Credit cards play an essential role in today’s digital economy, and their usage has recently grown tremendously, accompanied by a corresponding increase in credit card fraud. Machine learning (ML) algorithms have been utilized for credit card fraud detection. However, the dynamic shopping patterns of credit card holders and the class imbalance problem have made it difficult for ML classifiers to achieve optimal performance. This research project aims to develop a reliable credit card fraud detection system through a stacking ensemble method, integrating LSTM and Random Forest machine learning techniques. This approach aims to enhance fraud detection accuracy by leveraging the diverse strengths of both models. The system will undergo rigorous evaluation to ensure its efficiency in identifying fraudulent transactions while minimizing false positives. By combining the temporal sequencing capabilities of LSTM with the decision-making process of Random Forest, the proposed approach seeks to achieve heightened sensitivity to fraudulent patterns while maintaining computational efficiency. Ultimately, the objective is to bolster security measures and protect financial institutions and consumers from potential fraud risks.

Credit cards play an essential role in today’s digital economy, and their usage has recently grown tremendously, accompanied by a corresponding increase in credit card fraud. Machine learning (ML) algorithms have been utilized for credit card fraud detection. However, the dynamic shopping patterns of credit card holders and the class imbalance problem have made it difficult for ML classifiers to achieve optimal performance. This research project aims to develop a reliable credit card fraud detection system through a stacking ensemble method, integrating LSTM and Random Forest machine learning techniques. This approach aims to enhance fraud detection accuracy by leveraging the diverse strengths of both models. The system will undergo rigorous evaluation to ensure its efficiency in identifying fraudulent transactions while minimizing false positives. By combining the temporal sequencing capabilities of LSTM with the decision-making process of Random Forest, the proposed approach seeks to achieve heightened sensitivity to fraudulent patterns while maintaining computational efficiency. Ultimately, the objective is to bolster security measures and protect financial institutions and consumers from potential fraud risks.

Fraud Detection is of great importance to financial institutions. In this paper we have tried to study the Outlier Analysis in Stock Market Fraud Detection. Outlier Analysis is a fundamental issue in Data Mining, specifically in Fraud Detection. While observing the Indian Stock Market, we could detect that some of the Trading Entities have suspicious trading patterns that give rise to a doubt of having some malpractices in stock transactions within Indian Stock Market. All the facts are presented on the basis of data obtained from the official sites of BSE (Bombay Stock Exchange), NSE (National Stock Exchange) and SEBI (Securities and Exchange Board of India).

Fraud Detection is of great importance to financial institutions. In this paper we have tried to study the Outlier Analysis in Stock Market Fraud Detection. Outlier Analysis is a fundamental issue in Data Mining, specifically in Fraud Detection. While observing the Indian Stock Market, we could detect that some of the Trading Entities have suspicious trading patterns that give rise to a doubt of having some malpractices in stock transactions within Indian Stock Market. All the facts are presented on the basis of data obtained from the official sites of BSE (Bombay Stock Exchange), NSE (National Stock Exchange) and SEBI (Securities and Exchange Board of India).

Fraud Detection is of great importance to financial institutions. In this paper we have tried to study the Outlier Analysis in Stock Market Fraud Detection. Outlier Analysis is a fundamental issue in Data Mining, specifically in Fraud Detection. While observing the Indian Stock Market, we could detect that some of the Trading Entities have suspicious trading patterns that give rise to a doubt of having some malpractices in stock transactions within Indian Stock Market. All the facts are presented on the basis of data obtained from the official sites of BSE (Bombay Stock Exchange), NSE (National Stock Exchange) and SEBI (Securities and Exchange Board of India).

 This review paper define the way of fraud detection with the help of data mining techniques which summaries from different type of known fraud. Fraud detection includes monitoring of the behavior of user. Fraud is million dollar business and which increase every year very rapidly. This paper defines the techniques used for fraud detection. 

Before we get into fraud detection

Before we get into fraud detection

Klaassen in (Klaassen 2015) proposed a method for the detection of data manipulation given the means and standard deviations for the cells of a oneway ANOVA design. This comment critically reviews this method. In addition, inspired by this analysis, an alternative approach to test sample correlations over several experiments is derived. The results are in close agreement with the initial analysis reported by an anonymous whistlelblower. Importantly, the statistic requires several similar experiments; a test for correlations between 3 sample means based on a single experiment must be considered as unreliable.

Klaassen in (Klaassen 2015) proposed a method for the detection of data manipulation given the means and standard deviations for the cells of a oneway ANOVA design. This comment critically reviews this method. In addition, inspired by this analysis, an alternative approach to test sample correlations over several experiments is derived. The results are in close agreement with the initial analysis reported by an anonymous whistlelblower. Importantly, the statistic requires several similar experiments; a test for correlations between 3 sample means based on a single experiment must be considered as unreliable.

The research evaluates all recent applications of machine learning (ML) and deep learning (DL) for detecting credit card fraud. The study details multiple approaches to develop fraud detection systems by exploring both data quality enhancement methods along with feature selection approaches and modeling strategies.  The implementation of advanced deep learning approaches LSTM together with CNNs leads to high real-time detection of fraud because they excel at detecting sophisticated temporal sequences. XGBoost ensemble methods used with AdaBoost and SMOTE methods make great strides in improving fraud dataset handling of class imbalance issues. The method which is known as federated learning currently attracts attention because it helps institutions to collaborate on separate model training without exposing their actual data. Problems persist with the current development of fraud detection models since they need adaptable models for various datasets while also requiring interpretation capabilities and functionality that adapts to changing deceit patterns. The development of privacy-preserving methods for fraud detection must continue because they need to achieve sufficient efficiency and security standards for real-time applications. Problems with scalability in addition to unclear detection methods and adaptability require new research directions that fill existing knowledge gaps.

The research evaluates all recent applications of machine learning (ML) and deep learning (DL) for detecting credit card fraud. The study details multiple approaches to develop fraud detection systems by exploring both data quality enhancement methods along with feature selection approaches and modeling strategies.  The implementation of advanced deep learning approaches LSTM together with CNNs leads to high real-time detection of fraud because they excel at detecting sophisticated temporal sequences. XGBoost ensemble methods used with AdaBoost and SMOTE methods make great strides in improving fraud dataset handling of class imbalance issues. The method which is known as federated learning currently attracts attention because it helps institutions to collaborate on separate model training without exposing their actual data. Problems persist with the current development of fraud detection models since they need adaptable models for various datasets while also requiring interpretation capabilities and functionality that adapts to changing deceit patterns. The development of privacy-preserving methods for fraud detection must continue because they need to achieve sufficient efficiency and security standards for real-time applications. Problems with scalability in addition to unclear detection methods and adaptability require new research directions that fill existing knowledge gaps.

With the rapid expansion of online financial transactions, detecting fraudulent activity has become a significant concern. This study investigates the integration of scalable artificial intelligence AI technologies into fraud detection systems with a focus on maintaining a seamless and user friendly experience. By employing real time monitoring, intuitive alert systems, and machine learning algorithms, platforms can identify anomalous behaviors while minimizing disruption to users. The research emphasizes the need to balance robust security with usability, ensuring that fraud detection measures do not compromise transaction speed or user satisfaction. Additionally, the paper explores challenges such as alert fatigue, integration complexity, and privacy concerns, proposing solutions including adaptive learning models, blockchain integration, and collaborative frameworks with cybersecurity experts. The findings underscore the importance of designing fraud detection frameworks that are both scalable and responsive to evolving threats, without sacrificing the user experience. Dilip Kumar | Yashwant Kumar "Fraud Detection in Online Transactions: Enhancing User Experience with Scalable AI Solutions" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-9 | Issue-2 , April 2025, URL: https://www.ijtsrd.com/papers/ijtsrd78658.pdf Paper Url: https://www.ijtsrd.com/engineering/other/78658/fraud-detection-in-online-transactions-enhancing-user-experience-with-scalable-ai-solutions/dilip-kumar

Le président tanzanien John Magufuli a demandé une enquête sur le fonctionnement du Laboratoire national du pays, qualifiant de “douteux” les résultats d'un exercice destiné à tester l’efficacité de l’institution. Selon Magufuli, le ministère de la Santé du pays a envoyé des échantillons d’origine non humaine au laboratoire et certains d’entre eux se sont révélés positifs au c oronavirus . Ils comprennent des échantillons de fruits, d’animaux et d’autres fluides provenant d’autres organismes. Dans le même temps, la Tanzanie cherche également à obtenir l’assistance de Madagascar dans l’analyse des échantillons. Tanzanian President John Magufuli asked for an investigation into the operation of the country's National Laboratory, calling the results of an exercise to test the effectiveness of the institution "questionable." Magufuli said the country's health ministry has sent non-human samples to the lab, some of which have tested positive for coronavirus. They include samples of fruit, animals and other fluids from other organisms. At the same time, Tanzania is also seeking assistance from Madagascar in analyzing the samples.

Le président tanzanien John Magufuli a demandé une enquête sur le fonctionnement du Laboratoire national du pays, qualifiant de “douteux” les résultats d'un exercice destiné à tester l’efficacité de l’institution. Selon Magufuli, le ministère de la Santé du pays a envoyé des échantillons d’origine non humaine au laboratoire et certains d’entre eux se sont révélés positifs au c oronavirus . Ils comprennent des échantillons de fruits, d’animaux et d’autres fluides provenant d’autres organismes. Dans le même temps, la Tanzanie cherche également à obtenir l’assistance de Madagascar dans l’analyse des échantillons. Tanzanian President John Magufuli asked for an investigation into the operation of the country's National Laboratory, calling the results of an exercise to test the effectiveness of the institution "questionable." Magufuli said the country's health ministry has sent non-human samples to the lab, some of which have tested positive for coronavirus. They include samples of fruit, animals and other fluids from other organisms. At the same time, Tanzania is also seeking assistance from Madagascar in analyzing the samples.

Le président tanzanien John Magufuli a demandé une enquête sur le fonctionnement du Laboratoire national du pays, qualifiant de “douteux” les résultats d'un exercice destiné à tester l’efficacité de l’institution. Selon Magufuli, le ministère de la Santé du pays a envoyé des échantillons d’origine non humaine au laboratoire et certains d’entre eux se sont révélés positifs au c oronavirus . Ils comprennent des échantillons de fruits, d’animaux et d’autres fluides provenant d’autres organismes. Dans le même temps, la Tanzanie cherche également à obtenir l’assistance de Madagascar dans l’analyse des échantillons. Tanzanian President John Magufuli asked for an investigation into the operation of the country's National Laboratory, calling the results of an exercise to test the effectiveness of the institution "questionable." Magufuli said the country's health ministry has sent non-human samples to the lab, some of which have tested positive for coronavirus. They include samples of fruit, animals and other fluids from other organisms. At the same time, Tanzania is also seeking assistance from Madagascar in analyzing the samples.

Intense global and local technological augment and diminishing trade barriers are making life difficult for businesses. At a time when customers have revolutionary expectations, sophisticated decision system modeling is pivotal to an organization's business strategy. This era is e-payment focused, and emphasizes enhanced operational efficiencies. Other necessary attributes needed to compete successfully include facilitating customer services and satisfaction, risk management, decision support systems, major breakdown of trade barriers, enhanced security and privacy, market forces, better technology, communication etc. Electronic payment system is essential for proper functioning of the electronic market on the Internet. The significance of electronic payment system is enhanced by the effective use of credit card system at the electronic market online. However, the time has come to break down the psychological, culture and traditional barriers and adapt the value-added component of the secure electronic payment tool. Neural Network (NN) is a revolutionary changing process for online business trading and should be adopted for safety and security of one’s own business, and to join the global bandwagon of technical trends.

Intense global and local technological augment and diminishing trade barriers are making life difficult for businesses. At a time when customers have revolutionary expectations, sophisticated decision system modeling is pivotal to an organization's business strategy. This era is e-payment focused, and emphasizes enhanced operational efficiencies. Other necessary attributes needed to compete successfully include facilitating customer services and satisfaction, risk management, decision support systems, major breakdown of trade barriers, enhanced security and privacy, market forces, better technology, communication etc. Electronic payment system is essential for proper functioning of the electronic market on the Internet. The significance of electronic payment system is enhanced by the effective use of credit card system at the electronic market online. However, the time has come to break down the psychological, culture and traditional barriers and adapt the value-added component of the secure electronic payment tool. Neural Network (NN) is a revolutionary changing process for online business trading and should be adopted for safety and security of one’s own business, and to join the global bandwagon of technical trends.

Fraud Detection is a major concern these days because of digitalization. We are totally dependent on online transactions these days for even very small needs. There is no doubt that online transactions have made our life very easy but it has increased risk on other hand. And this risk can be very harmful one day. Confidential data is being stolen by the different apps and it is sold in international market. Which later on comes to us in totally different and very harmful way. So why not to use technology again to stop these risks and flaws. Various ML techniques has been observed by researchers but Auto ML is yet not discovered on a wider platform. Therefore, this paper at first aims to explore the trending technology Auto ML. Then a model for evaluating Auto ML is suggested and analysed with different classification algorithms. The experimental results ascertained the accuracy of Auto ML followed by a comparative analysis of ML and Auto ML.

Priestcraft in perfection: or, a detection of the fraud of inserting and continuing this clause (The Church hath power to decree rites and ceremonys, and authority in Controversys of faith) in the twentieth article of the Articles of the Church of England. 1710.. Digitized from IA40316005-33 . Previous issue: bim_eighteenth-century_an-essay-concerning-the-_collins-anthony_1709 . Next issue: bim_eighteenth-century_an-essay-on-contentment_gentleman-of-glasgow_1749 .

Priestcraft in perfection: or, a detection of the fraud of inserting and continuing this clause (The Church hath power to decree rites and ceremonys, and authority in Controversys of faith) in the twentieth article of the Articles of the Church of England. 1710.. Digitized from IA40316005-33 . Previous issue: bim_eighteenth-century_an-essay-concerning-the-_collins-anthony_1709 . Next issue: bim_eighteenth-century_an-essay-on-contentment_gentleman-of-glasgow_1749 .

Every year, millions of dollars are lost due to fraudulent credit card transactions. To help fraud investigators, more algorithms are turning to powerful machine learning methodologies. Designing fraud detection algorithms is particularly difficult because to the non-stationary distribution of data, excessively skewed class distributions, and continuous streams of transactions. At the same time, due to confidentiality considerations, public data is uncommon, leaving many questions unanswered about the best technique for dealing with them. We present some replies from the practitioners in this publication. Un balanced ness, non- stationarity and assessment. Our industrial partner provided us with an actual credit card dataset, which we used to do the analysis. In this project, we attempt to develop and evaluate a model for the imbalanced credit card fraud dataset.

Get a practical overview of Vertex AI capabilities by building a fraud detection system. Explore a guided tour of managed datasets, the feature store, pipelines, monitoring, workbench, and other Vertex AI Features. See a live demonstration of the resulting system, and reflect on some lessons learned while developing this app. Speaker: Ivan Nardini Watch more: All Google I/O 2022 Sessions → https://goo.gle/IO22_AllSessions Google Cloud at I/O 2022 playlist → https://goo.gle/IO22_GoogleCloud All Google I/O 2022 technical sessions → https://goo.gle/IO22_Sessions Subscribe to Google Cloud Tech → https://goo.gle/GoogleCloudTech #GoogleIO Source: https://www.youtube.com/watch?v=5kEhkKfs4TI Uploader: Google Cloud Tech

Investigation to quantify the likelihoods of outcomes of the 3-D Secure Fraud Detection backend, based on interventions on changing transaction value, machine data or the region.

The potential for fraud increases as digital payments replace traditional paper methods.

The Web-Based Online Auction Platform is an innovative e-commerce solution designed to facilitate secure and dynamic transactions between buyers and sellers. Supporting multiple auction formats, including Silent Auction, Buy-It-Now, and Standard Auction, the platform ensures flexibility and engagement for users. Real-time bidding updates are seamlessly integrated using Firebase, enhancing interaction and responsiveness. To strengthen security and trust, the system incorporates advanced Machine Learning (ML) and Deep Learning (DL) models, such as Random Forest, Isolation Forest, and Autoencoders, for fraud detection. These models analyze bidding behaviors, identify anomalies, and mitigate risks associated with fraudulent activities. For transaction security, the platform leverages Razor pay-powered escrow payments, ensuring that funds are securely held until both parties confirm the transaction, thereby minimizing disputes and enhancing user confidence. The platform is designed with a modular architecture, ensuring scalability and efficient handling of large user bases. This research highlights the system’s capability to offer a secure, interactive, and user- centric online auction experience with robust fraud detection mechanisms and transaction security features, making it an ideal solution for organizations and individuals seeking a reliable digital auction environment.

While many financiers use the Internet and social media to help them with investment decisions, these online tools can provide many benefits for investors and at the same time, same tools can make smart objectives for lawbreakers. These offenders are quick to adapt to new technologies - and Social media is no exception. Social media, such as Facebook, YouTube, Twitter, and LinkedIn, have become key tools for investors worldwide. Whether they are seeking study on particular stocks, background information on a broker-dealer or investment consultant, guidance on an overall investment strategy, up to date news or to simply want to discuss the markets with others, investors turn to social media. Social media also offers a number of features that criminals may find attractive. Fraudsters can use social media in their efforts to appear legitimate, to hide behind anonymity, and to reach many people at low cost. K Leena Kurien | Dr. Ajeet Chikkamannur"A Survey of Methodaology of Fraud Detection Using Data Mining" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-1 | Issue-6 , October 2017, URL: http://www.ijtsrd.com/papers/ijtsrd2482.pdf Article URL: http://www.ijtsrd.com/computer-science/data-miining/2482/a-survey-of-methodaology-of-fraud-detection-using-data-mining/k-leena-kurien

The usage of credit cards has increased significantly in everyday circumstances as a result of the Internet's rapid expansion and the simplicity of electronic the purpose of transmission. A credit card allows the individual using it to pay for goods or services purchased from a merchant, but because it operates on a cash-in-advance basis, the cardholders must return the funds that they have already spent after a brief amount of period. Credit cards are vulnerable to an array of threats, such as identity theft, phishing, skimming, and card absence before transaction. We can use systems that detect fraud to predict scams well in advance to enable to use credit cards securely and fraud free. Researchers have been using a number of credit card fraud detection techniques, include rule-based systems, internal fraud detection systems, and neural networks.   Credit card fraud is the fraudulent use of an individual's credit card or other data without the permission of the owner. People frequently get scammed and ripped off through significant fraud techniques such as application and behavioral fraud. Multiple applications produced by the same someone might result in identical fraud. Application fraud happens when scammers use fraudulent to apply for new cards from the bank.

Energy fraud in the distribution sector of electric utility includes electricity theft, meter tampering, or billing error. This fraud causing non-technical loss has led to an economic loss of the company. In order to detect and minimize fraud, different technologies have been used. From conventional methods to development in the field of artificial intelligence (AI), effective and reliable fraud detection methods have been proposed. This paper first provides an overview of different proposed methods for non-technical loss detection and evaluate the advantage and limitation of using those methods. Furthermore, several supervised and unsupervised machine learning methods for detecting electricity theft are discussed in summary along with their metrics and attributes used. Finally, these methods are classified based on the overall operation and the parameters used. This paper provides comparisons of several fraud detection methods using AI along with their weak and strong points and this information is very useful for the researchers who are working in the field of AI method for detecting fraud.

Due to increasing use of internet for the past several years there has been seen a drastic increase in electronic business. Without any face to face interaction electronicbusiness is carried out. Many Electronic Payment system or modes are developed such as credit cards, debit card. This has resulted in Electronic-Frauds. And day-byday electronic frauds are increasing rapidly, which causes capital loss to organizations as well as customers who uses credit cards and debit cards. This gave an urge to develop credit card fraud detection system. In this paper our goal is to reduce fraud effectively hence genetic algorithm is preferred. Based on number of iteration of user behaviour Genetic algorithm classifies the transaction as either Fraud or either authorized.

Due to increasing use of internet for the past several years there has been seen a drastic increase in electronic business. Without any face to face interaction electronicbusiness is carried out. Many Electronic Payment system or modes are developed such as credit cards, debit card. This has resulted in Electronic-Frauds. And day-byday electronic frauds are increasing rapidly, which causes capital loss to organizations as well as customers who uses credit cards and debit cards. This gave an urge to develop credit card fraud detection system. In this paper our goal is to reduce fraud effectively hence genetic algorithm is preferred. Based on number of iteration of user behaviour Genetic algorithm classifies the transaction as either Fraud or either authorized.

NA

Credit card fraud is a serious and growing problem. Due to a rapid advancement in the electronic commerce technology, the use of credit cards has dramatically increased. As credit card becomes the most popular mode of payment for both online as well as regular purchase, cases of fraud associated with it are also rising. In this paper, we model the sequence of operations in credit card transaction processing using a Hidden Markov Model (HMM) and show how it can be used for the detection of frauds. An HMM is initially trained with the normal behavior of a cardholder. If an incoming credit card transaction is not accepted by the trained HMM with a sufficiently high probability, it is considered to be fraudulent. At the same time, we try to ensure that genuine transactions are not rejected. We present detailed experimental results to show the effectiveness of our approach and compare it with other techniques available in the literature.

A blockchain network's economics and user confidence can be seriously harmed by fraud. Consensus algorithms like proof of work and proof of stake can verify the legitimacy of a transaction but not the identity of the people who are conducting or verifying it. On a blockchain network, fraud can still occur, as a result of this. One approach to fighting fraud is to make use of machine learning techniques. There are two types of machine learning: supervised and unsupervised. We use a variety of supervised machine learning techniques in this study to distinguish between legitimate and fraudulent purchases. We also compare decision trees, Naive Bayes, logistic regression, multilayer perceptron, and other supervised machine learning techniques in detail for this challenge

GAO-10-96 Border Security: Better Usage of Electronic Passport Security Features Could Improve Fraud Detection Published 22-Jan-10 http://www.gao.gov/new.items/d1096.pdf (Unofficial mirror of http://www.documentcloud.org/documents/242336-border-security-better-usage-of-electronic.html)

While many financiers use the Internet and social media to help them with investment decisions, these online tools can provide many benefits for investors and at the same time, same tools can make smart objectives for lawbreakers. These offenders are quick to adapt to new technologies '“ and Social media is no exception. Social media, such as Facebook, YouTube, Twitter, and LinkedIn, have become key tools for investors worldwide. Whether they are seeking study on particular stocks, background information on a broker-dealer or investment consultant, guidance on an overall investment strategy, up to date news or to simply want to discuss the markets with others, investors turn to social media. Social media also offers a number of features that criminals may find attractive. Fraudsters can use social media in their efforts to appear legitimate, to hide behind anonymity, and to reach many people at low cost. K Leena Kurien | Dr. Ajeet Chikkamannur"A Survey of Methodaology of Fraud Detection Using Data Mining" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-1 | Issue-6 , October 2017, URL: http://www.ijtsrd.com/papers/ijtsrd2482.pdf  http://www.ijtsrd.com/computer-science/data-miining/2482/a-survey-of-methodaology-of-fraud-detection-using-data-mining/k-leena-kurien

While many financiers use the Internet and social media to help them with investment decisions, these online tools can provide many benefits for investors and at the same time, same tools can make smart objectives for lawbreakers. These offenders are quick to adapt to new technologies - and Social media is no exception. Social media, such as Facebook, YouTube, Twitter, and LinkedIn, have become key tools for investors worldwide. Whether they are seeking study on particular stocks, background information on a broker-dealer or investment consultant, guidance on an overall investment strategy, up to date news or to simply want to discuss the markets with others, investors turn to social media. Social media also offers a number of features that criminals may find attractive. Fraudsters can use social media in their efforts to appear legitimate, to hide behind anonymity, and to reach many people at low cost. K Leena Kurien | Dr. Ajeet Chikkamannur"A Survey of Methodaology of Fraud Detection Using Data Mining" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-1 | Issue-6 , October 2017, URL: http://www.ijtsrd.com/papers/ijtsrd2482.pdf Article URL: http://www.ijtsrd.com/computer-science/data-miining/2482/a-survey-of-methodaology-of-fraud-detection-using-data-mining/k-leena-kurien

The Google Play- the foremost widespread automation app market where rank abuse and malware search has increased rapidly. In this paper, we have a tendency to introduce Fairplay, a unique system that discovers and traces malware left behind by fraudsters. The proposed system's aim is to discover malware and apps subjected to search rank fraud. Fairplay correlates review activities and unambiguously combines detected review relations with linguistic and behavioural signals obtained from Google Play app information. Fairplay achieves gold customary datasets of malware and we go for broad read by applying some technique to each application to gauge its ranking. Our necessity is to create a perfect, fraud less application. Fraudsters create fraud by downloading application through numerous devices and provide fraud ratings and reviews. So, we tend to aforesaid to mine crucial information relating to specific application through reviews that are acquired from comments. Later, these reviews are combined to mine fraud in application ranking. Prajakta Rane | Priya Mishra | Dr. Archana Chaugule"Search Rank Fraud and Malware Detection in Google Play" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-3 , April 2018, URL: http://www.ijtsrd.com/papers/ijtsrd11235.pdf http://www.ijtsrd.com/computer-science/data-miining/11235/search-rank-fraud-and-malware-detection-in-google-play/prajakta-rane

The Google Play- the foremost widespread automation app market where rank abuse and malware search has increased rapidly. In this paper, we have a tendency to introduce Fairplay, a unique system that discovers and traces malware left behind by fraudsters. The proposed system's aim is to discover malware and apps subjected to search rank fraud. Fairplay correlates review activities and unambiguously combines detected review relations with linguistic and behavioural signals obtained from Google Play app information. Fairplay achieves gold customary datasets of malware and we go for broad read by applying some technique to each application to gauge its ranking. Our necessity is to create a perfect, fraud less application. Fraudsters create fraud by downloading application through numerous devices and provide fraud ratings and reviews. So, we tend to aforesaid to mine crucial information relating to specific application through reviews that are acquired from comments. Later, these reviews are combined to mine fraud in application ranking. Prajakta Rane | Priya Mishra | Dr. Archana Chaugule"Search Rank Fraud and Malware Detection in Google Play" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-3 , April 2018, URL: http://www.ijtsrd.com/papers/ijtsrd11235.pdf http://www.ijtsrd.com/computer-science/data-miining/11235/search-rank-fraud-and-malware-detection-in-google-play/prajakta-rane

Fraud is an unlawful act committed by individuals, both from within and outside an organization, with the aim of obtaining personal or group benefits that directly harm others. Regional autonomy, which leads to the central government delegating authority to district/city areas, can also transfer the potential for fraud from the center to the regions. One of the government's efforts in preventing and detecting fraud is through audits; however, the performance of fraud detection is highly influenced by professional skepticism, auditor experience, and the limited time provided. This study aims to: (1) Analyze professional skepticism as an antecedent in an auditor's fraud detection. (2) Analyze the level of auditor experience in handling fraud, as not all auditors with experience uncover fraudulent activities. (3) Analyze the moderating role of time pressure in fraud detection. Data were obtained through observation, in-depth interviews, surveys, documentation, and the use of questionnaires. This research is a causal associative study. The total sample collected was 45 individuals, selected using convenience sampling. The results of this study indicate that: 1) Work Experience has a positive influence on Auditor's Ability to Detect Fraud. 2) Professional Skepticism has a positive influence on Auditor's Ability to Detect Fraud. 3) Time Pressure has a negative influence on Auditor's Ability to Detect Fraud. 4) Work Experience, Professional Skepticism, and Time Pressure collectively influence Auditor's Ability to Detect Fraud.

Introduction Extra Virgin Olive Oil (EVOO) is one of the most common and popular edible oils which is an important part of the Mediterranean diet. It is a rich source of sterol, phenol compounds and vitamins A and E. EVOO has useful effects on human body and significant reduction of cardiovascular diseases due to these benefits, EVOO is expensive so unfortunately adulteration in EVOO by mixing it with other cheap and low cost and low value oils such as canola, sunflower, palm and etc. is very common. Adulteration leads to health and financial losses and sometimes cause serious illness. Olive oil has various quality levels which depend on different factors such as olive cultivar, storage, oil extracting process etc. Materials and Methods There are numerous food quality evaluation and adulteration detection approaches which include destructive and non-destructive methods. Control sample (EVOO) was applied from "DANZEH food industry", Lowshan, Gilan Province. For ensuring that control sample is extra virgin, a sample was tested in "Rahpooyan e danesh koolak Lab." Tehran, Iran; according to "Institute of standards and industrial research of Iran" ISIRI number: 4091 and INSO 13126-2. Eight semi-conductor gas sensors "FIS, MQ3, MQ3, MQ4, MQ8, MQ135, MQ136, TGS136, TGS813 AND TGS822" applied in used olfaction machine. In this study there were 6 treatments: 1- Pure EVOO, 2- EVOO with 5% adulteration, 3- EVOO with 10% adulteration, 4- EVOO with 20% adulteration, 5- EVOO with 35% adulteration and 6- EVOO with 50% adulteration. Adulteration created with ordinary frying oil (including sunflower, canola, and maize oils). Each treatment prepared in seven samples and each sample test was repeated seven times. In this study, olfaction machine, a non-destructive, simple and user friendly System applied. As mentioned, the olfaction machine includes eight different sensors, so each test has eight graphs. Four features (1- Sensor output (mV) in start of odor pulse (refer to fig. 3) 2- Sensor output at the end of odor pulse 3- Average of sensor output during odor pulse and 4- Difference of sensor output at the end and start of start of odor pulse); So 32 features extracted and analyzed and finally effective sensors reported. Results and Discussion Histogram and box plot of raw data showed that the data are not normal and need some preprocessing operations. Preprocessing facilitates data analyzing and classifying extracted features. After preprocessing, the standard data, divided into two classes: train data (70%) and test data (30%). Data classified with 4 different classifier models which include: K-nearest neighbors, support vector machine, artificial neural network and Ada-boost. Results showed that KNN method, with 89.89% and SVM with 86.52% classified with higher accuracy. Similarly, the confusion matrix showed the reasonable results of classifying operation. Also, three effective sensors in classifying determined TGS2620, MQ5 and MQ4 respectively, and on the other side, sensors such as MQ3 and MQ8 have the minimum effect on classifying so it is possible to remove these sensors from the sensor array without effective impress on results. This may cause decrease in the olfaction machine price and reduce analyzing time. Conclusion Due to increasing adulteration in foods, especially in olive oil and its significant effects on people's health and financial losses, a simple, cheap and non-destructive quality evaluation extended. Results showed that the olfaction machine with metal oxide semiconductor (especially including TGS 2620, MQ5 and MQ4 sensors) can use for classification and adulteration detection of extra virgin olive oil. Evaluation of this system's output leads to higher classification accuracy by using KNN and SVM method for olive oil classification and also fraud detection (5% adulteration).

Fraudulent behaviors in Google Play, the most popular Android app market, fuel search rank abuse and malware proliferation. To identify malware, previous work has focused on app executable and permission analysis. In this paper, we introduce FairPlay, a novel system that discovers and leverages traces left behind by fraudsters, to detect both malware and apps subjected to search rank fraud. . Fair Play discovers hundreds of fraudulent apps that currently evade Google Bouncer’s detection technology. A. Brahma Reddy | K. V. Ranga Rao | V. Vinay Kumar "Fraud and Malware Detection in Google Play by using Search Rank" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-6 , October 2020, URL: https://www.ijtsrd.com/papers/ijtsrd35728.pdf Paper Url: https://www.ijtsrd.com/computer-science/computer-network/35728/fraud-and-malware-detection-in-google-play-by-using-search-rank/a-brahma-reddy

The Defense Finance Accounting Service DFAS-Operation Mongoose (Internal Review - Seaside) is using new and innovative techniques for fraud detection. Their primary techniques for fraud detection are the data mining tools of classification trees and neural networks as well as methods for pooling the results of multiple model fits. In this thesis a new data mining methodology, Multiple Additive Regression Trees (MART) is applied to the problem of detecting potential fraudulent and suspect transactions (those with conditions needing improvement - CNI's). The new MART methodology is an automated method for pooling a \"forest\" of hundreds of classification trees. This study shows how MART can be applied to fraud data. In particular it shows how MART identified classes of important variables and that MART is as effective with raw input variables as it is with the categorical variables currently constructed individually by DFAS. MART is also used to explore the effects of the substantial amount of missing data in the historical fraud database. In general MART is as accurate as existing methods, requires much less effort to implement saving many man-days, handles missing values in a sensible and transparent way, and provides features such as identifying more important variables.

The Defense Finance Accounting Service DFAS-Operation Mongoose (Internal Review - Seaside) is using new and innovative techniques for fraud detection. Their primary techniques for fraud detection are the data mining tools of classification trees and neural networks as well as methods for pooling the results of multiple model fits. In this thesis a new data mining methodology, Multiple Additive Regression Trees (MART) is applied to the problem of detecting potential fraudulent and suspect transactions (those with conditions needing improvement - CNI's). The new MART methodology is an automated method for pooling a \"forest\" of hundreds of classification trees. This study shows how MART can be applied to fraud data. In particular it shows how MART identified classes of important variables and that MART is as effective with raw input variables as it is with the categorical variables currently constructed individually by DFAS. MART is also used to explore the effects of the substantial amount of missing data in the historical fraud database. In general MART is as accurate as existing methods, requires much less effort to implement saving many man-days, handles missing values in a sensible and transparent way, and provides features such as identifying more important variables.

Fraudulent behaviors in Google Android app market fuel search rank abuse and malware proliferation. We present FairPlay, a novel system that uncovers both malware and search rank fraud apps, by picking out trails that fraudsters leave behind. To identify suspicious apps, FairPlay PCF algorithm correlates review activities and uniquely combines detected review relations with linguistic and behavioral signals gleaned from longitudinal Google Play app data. We contribute a new longitudinal app dataset to the community, which consists of over 87K apps, 2.9M reviews, and 2.4M reviewers, collected over half a year. FairPlay achieves over 95% accuracy in classifying gold standard datasets of malware, fraudulent and legitimate apps. We show that 75% of the identified malware apps engage in search rank fraud. FairPlay discovers hundreds of fraudulent apps that currently evade Google Bouncer detection technology, and reveals a new type of attack campaign, where users are harassed into writing positive reviews, and install and review other apps.