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

Inefficiencies in Digital Advertising Markets

Digital advertising markets are growing and attracting increased scrutiny. This article explores four market inefficiencies that remain poorly understood: ad effect measurement, frictions between and within advertising channel members, ad blocking, and ad fraud. Although these topics are not unique to digital advertising, each manifests in unique ways in markets for digital ads. The authors identify relevant findings in the academic literature, recent developments in practice, and promising topics for future research

The influence of mobile ad fraud on intercompany relationships : the case of Hang My Ads

Mestrado em MarketingDesde o seu início, a indústria da publicidade mobile tem vindo a enfrentar problemas de fraude associados a grandes perdas financeiras e danos na forma como as empresas se relacionam. O presente estudo explora os efeitos dos problemas de fraude nas relações entre empresas da indústria; abordando o ecosistema da publicidade das aplicações mobile, o contexto focal da Hang My Ads e os processos de adaptação necessários para lidar com os efeitos da fraude. O ecosistema da publicidade de aplicações mobile revela organizar-se em advertisers, intermediários, publishers e empresas de tecnologia, e é marcado por desafios como a fraude, a falta de transparência e a falta de regulamentação. Advertisers e publishers parecem adaptar-se de formas semelhantes, embora diferenças sejam detetadas nos processos de planeamento e agendamento do serviço, produção, e ?outro? ? onde advertisers adaptam mais e investem mais recursos; mas também ao nível de estrutura organizacional ? onde as adaptações parecem estar relacionadas com a dimensão da empresa. Além disto, a investigação confirma a ocorrência de adaptações ao nível da díade, que se propagam para a rede de empresas mais alargada. Além de perdas financeiras e baixo ROI, a realocação de orçamentos de acordo com a competência do publisher para lidar com fraude é confirmada; o estudo revela ainda como efeitos da fraude danos aos níveis da experiência do utilizador, da reputação da indústria e da eficiência das empresas. Um esquema visual do mapeamento do ecosistema e um modelo de análise modificado são propostos.Since its emergence, the mobile advertising industry has been struggling with fraud issues that cause great financial losses and damage how companies relate to one another. The present study exlores the effects of fraud issues taking place in the mobile advertising industry on intercompany relationships; particularly, it looks at the mobile app advertising ecosystem, the focal context of Hang My Ads and the adaptation processes undertaken by advertisers and publishers to tackle the effects of fraud. The mobile app advertising ecosystem is found to be organized in advertisers, intermediates, publishers and technology companies, and characterized by marking challenges such as fraud, lack of transparency and lack of regulation. Advertisers and publishers seem to adapt in similar ways to one another, but differences are found at the processes of service planning and scheduling, production, and "other" - where advertisers adapt more and seem to invest more resources; and at the level of organization structure - where adaptations appear to be related with company size. Furthermore, the case confirms the occurrence of adaptations taking place in the dyad and propagating to the broader network. In addition to financial losses and poor ROI, the reallocation of budgets according to a publisher's competence to handle fraud is confirmed; moreover, it is found that damages at the levels of user experience, industry's reputation and companies' efficiency are caused by fraud. A visual scheme of the ecosystem's mapping and a modified framework of analysis are proposed.info:eu-repo/semantics/publishedVersio

MadDroid: Characterising and Detecting Devious Ad Content for Android Apps

Advertisement drives the economy of the mobile app ecosystem. As a key component in the mobile ad business model, mobile ad content has been overlooked by the research community, which poses a number of threats, e.g., propagating malware and undesirable contents. To understand the practice of these devious ad behaviors, we perform a large-scale study on the app contents harvested through automated app testing. In this work, we first provide a comprehensive categorization of devious ad contents, including five kinds of behaviors belonging to two categories: \emph{ad loading content} and \emph{ad clicking content}. Then, we propose MadDroid, a framework for automated detection of devious ad contents. MadDroid leverages an automated app testing framework with a sophisticated ad view exploration strategy for effectively collecting ad-related network traffic and subsequently extracting ad contents. We then integrate dedicated approaches into the framework to identify devious ad contents. We have applied MadDroid to 40,000 Android apps and found that roughly 6\% of apps deliver devious ad contents, e.g., distributing malicious apps that cannot be downloaded via traditional app markets. Experiment results indicate that devious ad contents are prevalent, suggesting that our community should invest more effort into the detection and mitigation of devious ads towards building a trustworthy mobile advertising ecosystem.Comment: To be published in The Web Conference 2020 (WWW'20

Network On Network for Tabular Data Classification in Real-world Applications

Tabular data is the most common data format adopted by our customers ranging from retail, finance to E-commerce, and tabular data classification plays an essential role to their businesses. In this paper, we present Network On Network (NON), a practical tabular data classification model based on deep neural network to provide accurate predictions. Various deep methods have been proposed and promising progress has been made. However, most of them use operations like neural network and factorization machines to fuse the embeddings of different features directly, and linearly combine the outputs of those operations to get the final prediction. As a result, the intra-field information and the non-linear interactions between those operations (e.g. neural network and factorization machines) are ignored. Intra-field information is the information that features inside each field belong to the same field. NON is proposed to take full advantage of intra-field information and non-linear interactions. It consists of three components: field-wise network at the bottom to capture the intra-field information, across field network in the middle to choose suitable operations data-drivenly, and operation fusion network on the top to fuse outputs of the chosen operations deeply. Extensive experiments on six real-world datasets demonstrate NON can outperform the state-of-the-art models significantly. Furthermore, both qualitative and quantitative study of the features in the embedding space show NON can capture intra-field information effectively