FraudDroid: Automated Ad Fraud Detection for Android Apps

Although mobile ad frauds have been widespread, state-of-the-art approaches in the literature have mainly focused on detecting the so-called static placement frauds, where only a single UI state is involved and can be identified based on static information such as the size or location of ad views. Other types of fraud exist that involve multiple UI states and are performed dynamically while users interact with the app. Such dynamic interaction frauds, although now widely spread in apps, have not yet been explored nor addressed in the literature. In this work, we investigate a wide range of mobile ad frauds to provide a comprehensive taxonomy to the research community. We then propose, FraudDroid, a novel hybrid approach to detect ad frauds in mobile Android apps. FraudDroid analyses apps dynamically to build UI state transition graphs and collects their associated runtime network traffics, which are then leveraged to check against a set of heuristic-based rules for identifying ad fraudulent behaviours. We show empirically that FraudDroid detects ad frauds with a high precision (93%) and recall (92%). Experimental results further show that FraudDroid is capable of detecting ad frauds across the spectrum of fraud types. By analysing 12,000 ad-supported Android apps, FraudDroid identified 335 cases of fraud associated with 20 ad networks that are further confirmed to be true positive results and are shared with our fellow researchers to promote advanced ad fraud detectionComment: 12 pages, 10 figure

Fighting Online Click-Fraud Using Bluff Ads

Online advertising is currently the greatest source of revenue for many Internet giants. The increased number of specialized websites and modern profiling techniques, have all contributed to an explosion of the income of ad brokers from online advertising. The single biggest threat to this growth, is however, click-fraud. Trained botnets and even individuals are hired by click-fraud specialists in order to maximize the revenue of certain users from the ads they publish on their websites, or to launch an attack between competing businesses. In this note we wish to raise the awareness of the networking research community on potential research areas within this emerging field. As an example strategy, we present Bluff ads; a class of ads that join forces in order to increase the effort level for click-fraud spammers. Bluff ads are either targeted ads, with irrelevant display text, or highly relevant display text, with irrelevant targeting information. They act as a litmus test for the legitimacy of the individual clicking on the ads. Together with standard threshold-based methods, fake ads help to decrease click-fraud levels.Comment: Draf

Whose Click Fraud Data Do You Trust? Effect Of Click Fraud On Advertiser’s Trust And Sponsored Search Advertising Decisions

Online sponsored search has emerged as a dominant business model for majority of search engines and as a popular advertising mechanism for online retailers. However, sponsored search advertising is being negatively impacted by click fraud which involves the intentional clicking on sponsored links with the purpose of gaining undue monetary returns for the search engine or harming a particular advertiser by depleting its advertising budget. While search engines tend to compensate advertisers to an extent for click frauds, it still leaves an element of uncertainty in the minds of advertisers whether search engine is being faithful in reporting the click fraud numbers. Armed with additional data available from third party click fraud audit companies, advertisers may have more reasons to suspect click fraud numbers reported by search engines if there is a discrepancy between the numbers reported by two sources (search engines and third party click fraud audit companies). While the phenomenon of click fraud has been acknowledged to exist, its effect on sponsored search advertisers’ trust and their decision to advertise with a particular search engine has not been given sufficient attention in the literature. As an initial step, in this research in progress study, we develop a theoretical model to examine the effect of click fraud on advertiser’s trust in search engine and its subsequent impact on advertiser’s decision to adjust advertising spend for different search engines. In this paper, we also outline the proposed experimental design to validate the theoretical model subsequently in future. Broadly, the research suggests that sponsored search advertisers are likely to adjust their advertising spend based on level of trust they have in search engine, click fraud numbers discrepancy, and return on investment obtained from advertising on that particular search engine

CONTACTLESS PAYMENTS FRAUD DETECTION METHODS AND IS SOCIETY PREPARED TO RESIST: A CASE STUDY

The ability to use contactless payment technologies, non-cash payments and credit card payments is becoming almost an essential requirement for consumers and merchants in today's economic conditions. Different market sectors are rapidly adapting to these technologies and looking for the most convenient, secure, and fastest possible solutions that combine intelligent data processing, security, and business management functions. Millions of debit and credit card holders care about secure payments, the businesses that receive these payments are secure in terms of security, and the operators that process such incoming and outgoing payments are interested in innovative solutions that set them apart from the competition. Amid the COVID-19 pandemic, when e-commerce was growing exponentially, the global market for fraud detection and prevention, currently stands at USD 20.9 billion, and is expected to grow, until 2025 will rise to USD 38.2 billion by the end of the year; holds the market at 12.8 % annually. The US remains the dominant region in this market segment, but European countries are also increasingly investing in fraud prevention and detection solutions, which are growing in demand in Europe due to an increase in cybercrime as well as advanced bots and cyber-attack.

Who Blows the Whistle on Corporate Fraud?

What external control mechanisms are most effective in detecting corporate fraud? To address this question we study in depth all reported cases of corporate fraud in companies with more than 750 million dollars in assets between 1996 and 2004. We find that fraud detection does not rely on one single mechanism, but on a wide range of, often improbable, actors. Only 6% of the frauds are revealed by the SEC and 14% by the auditors. More important monitors are media (14%), industry regulators (16%), and employees (19%). Before SOX, only 35% of the cases were discovered by actors with an explicit mandate. After SOX, the performance of mandated actors improved, but still account for only slightly more than 50% of the cases. We find that monetary incentives for detection in frauds against the government influence detection without increasing frivolous suits, suggesting gains from extending such incentives to corporate fraud more generally.

Pump and Dumps in the Bitcoin Era: Real Time Detection of Cryptocurrency Market Manipulations

In the last years, cryptocurrencies are increasingly popular. Even people who are not experts have started to invest in these securities and nowadays cryptocurrency exchanges process transactions for over 100 billion US dollars per month. However, many cryptocurrencies have low liquidity and therefore they are highly prone to market manipulation schemes. In this paper, we perform an in-depth analysis of pump and dump schemes organized by communities over the Internet. We observe how these communities are organized and how they carry out the fraud. Then, we report on two case studies related to pump and dump groups. Lastly, we introduce an approach to detect the fraud in real time that outperforms the current state of the art, so to help investors stay out of the market when a pump and dump scheme is in action.Comment: Accepted for publication at The 29th International Conference on Computer Communications and Networks (ICCCN 2020

Are black friday deals worth it? Mining twitter users' sentiment and behavior response

The Black Friday event has become a global opportunity for marketing and companies’ strategies aimed at increasing sales. The present study aims to understand consumer behavior through the analysis of user-generated content (UGC) on social media with respect to the Black Friday 2018 offers published by the 23 largest technology companies in Spain. To this end, we analyzed Twitter-based UGC about companies’ offers using a three-step data text mining process. First, a Latent Dirichlet Allocation Model (LDA) was used to divide the sample into topics related to Black Friday. In the next step, sentiment analysis (SA) using Python was carried out to determine the feelings towards the identified topics and offers published by the companies on Twitter. Thirdly and finally, a data-text mining process called textual analysis (TA) was performed to identify insights that could help companies to improve their promotion and marketing strategies as well as to better understand the customer behavior on social media. The results show that consumers had positive perceptions of such topics as exclusive promotions (EP) and smartphones (SM); by contrast, topics such as fraud (FA), insults and noise (IN), and customer support (CS) were negatively perceived by customers. Based on these results, we offer guidelines to practitioners to improve their social media communication. Our results also have theoretical implications that can promote further research in this area