Click Fraud Detection in Online and In-app Advertisements: A Learning Based Approach

Click Fraud is the fraudulent act of clicking on pay-per-click advertisements to increase a site’s revenue, to drain revenue from the advertiser, or to inflate the popularity of content on social media platforms. In-app advertisements on mobile platforms are among the most common targets for click fraud, which makes companies hesitant to advertise their products. Fraudulent clicks are supposed to be caught by ad providers as part of their service to advertisers, which is commonly done using machine learning methods. However: (1) there is a lack of research in current literature addressing and evaluating the different techniques of click fraud detection and prevention, (2) threat models composed of active learning systems (smart attackers) can mislead the training process of the fraud detection model by polluting the training data, (3) current deep learning models have significant computational overhead, (4) training data is often in an imbalanced state, and balancing it still results in noisy data that can train the classifier incorrectly, and (5) datasets with high dimensionality cause increased computational overhead and decreased classifier correctness -- while existing feature selection techniques address this issue, they have their own performance limitations. By extending the state-of-the-art techniques in the field of machine learning, this dissertation provides the following solutions: (i) To address (1) and (2), we propose a hybrid deep-learning-based model which consists of an artificial neural network, auto-encoder and semi-supervised generative adversarial network. (ii) As a solution for (3), we present Cascaded Forest and Extreme Gradient Boosting with less hyperparameter tuning. (iii) To overcome (4), we propose a row-wise data reduction method, KSMOTE, which filters out noisy data samples both in the raw data and the synthetically generated samples. (iv) For (5), we propose different column-reduction methods such as multi-time-scale Time Series analysis for fraud forecasting, using binary labeled imbalanced datasets and hybrid filter-wrapper feature selection approaches

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

Link-based similarity search to fight web spam

www.ilab.sztaki.hu/websearch We investigate the usability of similarity search in fighting Web spam based on the assumption that an unknown spam page is more similar to certain known spam pages than to honest pages. In order to be successful, search engine spam never appears in isolation: we observe link farms and alliances for the sole purpose of search engine ranking manipulation. The artificial nature and strong inside connectedness however gave rise to successful algorithms to identify search engine spam. One example is trust and distrust propagation, an idea originating in recommender systems and P2P networks, that yields spam classificators by spreading information along hyperlinks from white and blacklists. While most previous results use PageRank variants for propagation, we form classifiers by investigating similarity top lists of an unknown page along various measures such as co-citation, companion, nearest neighbors in low dimensional projections and SimRank. We test our method over two data sets previously used to measure spam filtering algorithms. 1

Display Advertising with Real-Time Bidding (RTB) and Behavioural Targeting

The most significant progress in recent years in online display advertising is what is known as the Real-Time Bidding (RTB) mechanism to buy and sell ads. RTB essentially facilitates buying an individual ad impression in real time while it is still being generated from a user’s visit. RTB not only scales up the buying process by aggregating a large amount of available inventories across publishers but, most importantly, enables direct targeting of individual users. As such, RTB has fundamentally changed the landscape of digital marketing. Scientifically, the demand for automation, integration and optimisation in RTB also brings new research opportunities in information retrieval, data mining, machine learning and other related fields. In this monograph, an overview is given of the fundamental infrastructure, algorithms, and technical solutions of this new frontier of computational advertising. The covered topics include user response prediction, bid landscape forecasting, bidding algorithms, revenue optimisation, statistical arbitrage, dynamic pricing, and ad fraud detection

Awareness and perception of phishing variants from Policing, Computing and Criminology students in Canterbury Christ Church University

This study focuses on gauging awareness of different phishing communication students in the School of Law, Policing and Social Sciences and the School of Engineering, Technology and Design in Canterbury Christ Church University and their perception of different phishing variants. There is an exploration of the underlying factors in which students fall victim to different types of phishing attacks from questionnaires and a focus group. The students’ perception of different types of phishing variants was varied from the focus group and anonymised questionnaires. A total of 177 respondents participated in anonymised questionnaires in the study. Students were asked a mixture of scenario-based questions on different phishing attacks, their awareness levels of security tools that can be used against some phishing variants, and if they received any phishing emails in the past. Additionally, 6 computing students in a focus group discussed different types of phishing attacks and recommended potential security countermeasures against them. The vulnerabilities and issues of anti-phishing software, firewalls, and internet browsers that have security toolbars are explained in the study against different types of phishing attacks. The focus group was with computing students and their knowledge about certain phishing variants was limited. The discussion within the focus group was gauging the computing students' understanding and awareness of phishing variants. The questionnaire data collection sample was with first year criminology and final year policing students which may have influenced the results of the questionnaire in terms of their understanding, security countermeasures, and how they identify certain phishing variants. The anonymised questionnaire awareness levels on different types of phishing fluctuated in terms of lack of awareness on certain phishing variants. Some criminology and policing students either did not know about phishing variants or had limited knowledge about different types of phishing communication, security countermeasures, the identifying features of a phishing message, and the precautions they should take against phishing variants from fraudsters

Display Advertising with Real-Time Bidding (RTB) and Behavioural Targeting

The most significant progress in recent years in online display advertising is what is known as the Real-Time Bidding (RTB) mechanism to buy and sell ads. RTB essentially facilitates buying an individual ad impression in real time while it is still being generated from a user’s visit. RTB not only scales up the buying process by aggregating a large amount of available inventories across publishers but, most importantly, enables direct targeting of individual users. As such, RTB has fundamentally changed the landscape of digital marketing. Scientifically, the demand for automation, integration and optimisation in RTB also brings new research opportunities in information retrieval, data mining, machine learning and other related fields. In this monograph, an overview is given of the fundamental infrastructure, algorithms, and technical solutions of this new frontier of computational advertising. The covered topics include user response prediction, bid landscape forecasting, bidding algorithms, revenue optimisation, statistical arbitrage, dynamic pricing, and ad fraud detection

Data Scams

Targeting platforms like Google and Facebook are usually seen as presenting tradeoffs between utility and privacy. This Article identifies and describes a different, non-privacy cost of targeting platforms: they make it easier for malicious actors to scam others. They do this by making it easier for scammers to reach the most promising victims, hide from law-enforcement authorities and others, and develop better scams. Technology offers potential solutions, since the same data and targeting tools that enable scams could help detect and prevent them, though neither platforms nor law-enforcement officials have both the incentives and expertise needed to develop and deploy those solutions. Moreover, these scams may illustrate a broader class of problems from targeting that go beyond utility versus privacy, suggesting that more aggressive interventions may be needed