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This study explores how disinformation can dampen general users' expressions of opinion online. In the context of a proven disinformation case in South Korea, this study analyzes externally validated click-logs of 1389 fake accounts and more than a million logs of 45,769 general users in a highly popular web portal. Findings show that the inflated visibility of anti-governmental opinions in the manipulated comment space was incongruent with the overall political tone that general users had spontaneously encountered from the broader media ecosystem beyond the manipulated space. Subsequently, this opinion "climate" incongruence decreased the likelihood of commenting in the manipulated space. The study concludes that false amplification (of the opinions that the manipulators promote) and false diminution (of general users' political expressions) work in tandem to create a distorted opinion environment. [ABSTRACT FROM AUTHOR]

Advertising corporations have moved their focus to online and in-App advertisements in response to the expansion of digital technologies and social media. Online advertising represents the primary revenue source for advertising networks that serve as the middlemen between advertisers and advertisement publishers. The advertising networks pay the publisher of the advertisement based on the number of clicks through to advertisers, following the pay-per-click (PPC) payment scheme. However, there is a growing security issue with this payment approach known as Click Fraud. Click fraud is the illegal process of clicking on pay-per-click advertisements to increase publishers' revenue or deplete advertisers' budgets. Artificial intelligence techniques have been increasingly employed to solve complicated challenges in different research areas, including cybersecurity, to achieve unexpected outcomes. In this paper, several Machine Learning models were constructed to establish whether the user is a human or a bot and conducted a comparative performance analysis using a set of evaluation metrics. We used a real captured dataset detailing Internet users' behavior while navigating websites. We extracted a set of features related to users’ behaviors, including the number of webpages viewed during the browsing session, the duration of the browsing journey, and the actions performed. The empirical results revealed that all the considered models obtained good results, where the random forest algorithm surpassed all other algorithms in all evaluation metrics.

In online advertising, a change in the publisher's actual status label with every generated click shows the suspicious behaviour of the publisher. Furthermore, only a small proportion of the clicks generated by the publishers are invalid, resulting in class skewness in the dataset and a challenging issue for the conventional classification methods as they get biased towards the outnumbered class. This suspicious behaviour of publishers with an uneven class distribution ratio adversely affects the classifier's performance and increases model complexities. Thus, developing machine-learning methods capable of producing efficacious predictive models towards detecting fraudulent publishers is pivotal. This paper's novel stacked generalization framework comprises two stacked generalization architectures, one for resampling and the second for classification. The framework employs a stacked generalization approach using generalizers to improve the learning model's performance in two steps: first, reducing the error rate of algorithms towards reducing the bias in a learning set. Second, the results obtained through level-0 generalizers are fed as input to the level-1 generalizer with stacked integrated output towards combining the predictions for improving the predictive performance. Broad experimentations are conducted on FDMA 2012 user click dataset using ten-fold cross-validation. The performance of the proposed architecture is generalized by performing experiments on eight other highly imbalanced benchmark datasets, and performance is measured using average precision, recall, and F1-score. Results empirically prove the superiority of the proposed architecture in the publisher's behaviour prediction and classification as legitimate or illegitimate. [ABSTRACT FROM AUTHOR]

Advertising corporations have moved their focus to online and in-App advertisements in response to the expansion of digital technologies and social media. Online advertising represents the primary revenue source for advertising networks that serve as the middlemen between advertisers and advertisement publishers. The advertising networks pay the publisher of the advertisement based on the number of clicks through to advertisers, following the pay-per-click (PPC) payment scheme. However, there is a growing security issue with this payment approach known as Click Fraud. Click fraud is the illegal process of clicking on pay-per-click advertisements to increase publishers' revenue or deplete advertisers' budgets. Artificial intelligence techniques have been increasingly employed to solve complicated challenges in different research areas, including cybersecurity, to achieve unexpected outcomes. In this paper, several Machine Learning models were constructed to establish whether the user is a human or a bot and conducted a comparative performance analysis using a set of evaluation metrics. We used a real captured dataset detailing Internet users' behavior while navigating websites. We extracted a set of features related to users' behaviors, including the number of webpages viewed during the browsing session, the duration of the browsing journey, and the actions performed. The empirical results revealed that all the considered models obtained good results, where the random forest algorithm surpassed all other algorithms in all evaluation metrics. [ABSTRACT FROM AUTHOR]

Online advertising is a marketing approach that uses numerous online channels to target potential customers for businesses, brands, and organizations. One of the most serious threats in today's marketing industry is the widespread attack known as click fraud. Traffic statistics for online advertisements are artificially inflated in click fraud. Typical pay-per-click advertisements charge a fee for each click, assuming that a potential customer was drawn to the ad. Click fraud attackers create the illusion that a significant number of possible customers have clicked on an advertiser's link by an automated script, a computer program, or a human. Nevertheless, advertisers are unlikely to profit from these clicks. Fraudulent clicks may be involved to boost the revenues of an ad hosting site or to spoil an advertiser's budget. Several notable attempts to detect and prevent this form of fraud have been undertaken. This study examined all methods developed and published in the previous 10 years that primarily used artificial intelligence (AI), including machine learning (ML) and deep learning (DL), for the detection and prevention of click fraud. Features that served as input to train models for classifying ad clicks as benign or fraudulent, as well as those that were deemed obvious and with critical evidence of click fraud, were identified, and investigated. Corresponding insights and recommendations regarding click fraud detection using AI approaches were provided. [ABSTRACT FROM AUTHOR]

One of the challenging issues in user‐click data of online advertising is the uneven class distribution which biases classification models. Resampling the data is a popular choice for obtaining class balance. However, oversampling results in overfitting, whilst under‐sampling results in information loss. Moreover, enhancing separability between samples, where the classes overlap closer to the decision boundary, is another challenge, which requires a careful pruning of instances towards increasing the separability in data space. Therefore, in this work, a new hybrid data sampling algorithm SMOTEOSS is designed and evaluated, concatenating the synthetic minority oversampling technique (SMOTE) followed by one‐sided selection (OSS) to balance the class distribution. The working of SMOTEOSS is twofold‐ first, it oversamples the under‐represented class distribution using the SMOTE by generating synthetic instances. However, the generation of synthetic instances closer to the decision boundary directly influences the learning model's decision‐making. Utilising OSS, the proposed method then identifies TOMEKLINKS and eliminates the noisy majority instances whilst eliminating the redundant instances. The proposed method's effectiveness is validated on the FDMA 2012 dataset against 10 state‐of‐the‐art sampling methods utilising the gradient tree boosting learning model. To authenticate SMOTEOSS, a fair comparison is made by conducting experiments on other 10 benchmark imbalanced datasets using 10‐fold cross‐validation. Performance is measured using average precision, recall, F1‐score, G‐mean, the area under curve (AUC) and reduction rate. Results showed that the designed hybrid methodology is an efficient alternative to existing sampling methods. The Wilcoxon signed‐rank test is employed to demonstrate significant differences amidst the proposed and conventional sampling algorithms. [ABSTRACT FROM AUTHOR]

Click fraud is the sort of deception in which traffic figures for online ads are intentionally inflated. For businesses that advertise online, click fraud may occur often, resulting in erroneous click statistics and lost funds. That is why many businesses are hesitant to advertise their products on websites and mobile apps. To market their products safely, businesses need a reliable technique for detecting click fraud. In this paper we present a stacking algorithm as a solution to this problem. The proposed method's premise is to combine multiple learners to achieve an optimal result. The Synthetic Minority Oversampling Technique (SMOTE) with a combination of undersampling are chosen to handle the unbalanced dataset. In the first-level learners, there are four supervised Machine Learning algorithms, which are AdaBoost, Random Forest, Decision Tree and Logistic Regression. Moreover, Logistic Regression is used again as a the second-level learner. To verify the efficacy of the suggested approach, comparative tests are carried out on the public dataset available on Kaggle from China's largest independent big data service platform TalkingData. Multiple indicators, such as Accuracy, F1 Score, ROC curve, Loss Log and AUC Score, are utilized to analyze the prediction outcomes. The findings reveal that the stacking method improves forecast accuracy while also maintaining a high level of stability. [ABSTRACT FROM AUTHOR]

With the rapid development of online advertising, click fraud is a serious issue for the internet market. Click fraud is a dishonest attempt to improve a website’s profit or deplete an advertiser’s budget by clicking on pay-per-click advertisements. For an extended period, this illegal act has a threat to the industrial sectors. As a result, these businesses hesitate to advertise their items on mobile apps and websites, as numerous groups attempt to take advantage of themes. To safely advertise their services and products online, a robust mechanism is needed for efficient click fraud detection. To tackle this issue, an ensemble architecture of machine learning and deep learning is proposed to detect click fraud in online advertisement campaigns. The proposed ensemble architecture consists of a Convolutional Neural Network (CNN), and a Bidirectional Long Short-Term Memory network (BiLSTM) is used to extract hidden features, while the Random Forest (RF) is used for classification. The main objective of the proposed research study is to develop a hybrid DL model for automatic feature extraction from clicks data and then process through an RF classifier into two classes, such as fraudulent and non-fraudulent clicks. Furthermore, a preprocessing module is developed to preprocess data by dealing with categorical attributes and imbalanced data to enhance the reliability and consistency of the clicks data. In addition, different evaluation criteria are used to evaluate and compare the performance of the proposed CNN-BiLSTM-RF with the ensemble and standalone models. The experimental results indicate that our ensemble architecture achieved the accuracy of 99.19 ± 0.08%, precision 99.89 ± 0.03%, sensitivity 98.50 ± 0.11%, F1-score 99.19 ± 0.08% and specificity 99.89 ± 0.03%. Furthermore, our proposed architecture produced superior results compared to other developed ensemble and conventional models. Moreover, our proposed ensemble architecture can be used as a safeguard against click fraud for pay-per-click advertising to facilitate industries for the safe and reliable promotion of their products.

In the pay-per-click online advertisement model, fraudulent publishers' presence is rare than that of genuine publishers. This high-class imbalance between fraudulent and genuine publishers poses a challenge for the accurate classification of fraudsters due to the bias of automated learning models towards the outnumbered class. In this work, an empirical evaluation of significant popular data sampling methods is carried out using nine state-of-the-art learning models for classifying fraudsters in online advertisement. The main objective of this work is to understand the effect of oversampling, under-sampling, and hybrid sampling methods on the performance of various classifiers in click fraud detection. Extensive experiments are performed on the benchmark FDMA-2012 user-click dataset. The performance of each combination of data sampling method and classifiers is validated using average precision, recall, f1-score, and AUC. The results are also compared with the existing state-of-the-art models. The results suggest that adaptive synthetic sampling (ADASYN) oversampling with a gradient tree boosting (GTB) model performs best with an average precision score of 64.32%. [ABSTRACT FROM AUTHOR]

Online advertising utilizes the Internet technique to deliver marketing messages to promotional consumers. It is growing in recent years to facilitate the increasing demands of electronic commerce. Advertisers bid and pay for the advertisement whenever potential customers click it. The way of Pay-Per-Click (PPC) is vulnerable to malicious clicks that mimic real user behaviour to trick the platform into counting their clicks as legitimate. It causes massive financial losses on advertisers and also significantly reduces the credibility of online advertising platforms. The common strategies to detect fraud clicks are dynamically tailoring and interpreting data based on the machine learning model. These algorithms treat multi-dimensional data as an individual feature vector or matrix, making it different to explore intrinsic relations among a sequence of data. Million daily fraud clicks on various types further disperse the focus of models and result in relatively low efficiency for the current fraud prediction system. To tackle the fraud click problem, we introduce a tensor-based mechanism to predict fraud clicks. This paper considered reconstructing data into a high-rank tensor, implement tensor decomposition and transformation to explore hidden information under each data and explore the joined effect among a sequence of data. The proposed tensor transformation algorithm with locality-sensitive hashing (LSH) is tested by extensive experiments using real-world data. Compared with the state-of-art machine learning algorithms, our model can achieve significant performance in terms of accuracy and prediction-recall rate. [ABSTRACT FROM AUTHOR]

Online advertising is a marketing approach that uses numerous online channels to target potential customers for businesses, brands, and organizations. One of the most serious threats in today’s marketing industry is the widespread attack known as click fraud. Traffic statistics for online advertisements are artificially inflated in click fraud. Typical pay-per-click advertisements charge a fee for each click, assuming that a potential customer was drawn to the ad. Click fraud attackers create the illusion that a significant number of possible customers have clicked on an advertiser’s link by an automated script, a computer program, or a human. Nevertheless, advertisers are unlikely to profit from these clicks. Fraudulent clicks may be involved to boost the revenues of an ad hosting site or to spoil an advertiser’s budget. Several notable attempts to detect and prevent this form of fraud have been undertaken. This study examined all methods developed and published in the previous 10 years that primarily used artificial intelligence (AI), including machine learning (ML) and deep learning (DL), for the detection and prevention of click fraud. Features that served as input to train models for classifying ad clicks as benign or fraudulent, as well as those that were deemed obvious and with critical evidence of click fraud, were identified, and investigated. Corresponding insights and recommendations regarding click fraud detection using AI approaches were provided.

Abstract Mobile ads are plagued with fraudulent clicks which is a major challenge for the advertising community. Although popular ad networks use many techniques to detect click fraud, they do not protect the client from possible collusion between publishers and ad networks. In addition, ad networks are not able to monitor the user’s activity for click fraud detection once they are redirected to the advertising site after clicking the ad. We propose a new crowdsource-based system called Click Fraud Crowdsourcing (CFC) that collaborates with both advertisers and ad networks in order to protect both parties from any possible click fraudulent acts. The system benefits from both a global view, where it gathers multiple ad requests corresponding to different ad network-publisher-advertiser combinations, and a local view, where it is able to track the users’ engagement in each advertising website. The results demonstrated that our approach offers a lower false positive rate (0.1) when detecting click fraud as opposed to proposed solutions in the literature, while maintaining a high true positive rate (0.9). Furthermore, we propose a new mobile ad charging model that benefits from our system to charge advertisers based on the duration spent in the advertiser’s website.

Today, mobile advertising is considered as the most effective medium to convey promotional messages to customers because of the excessive usage of mobile phones and tablets all around the world. However, this ecosystem has severely been affected by fraudulent activities due to a large sum of money circulated in the advertising industry. The term ad fraud is referred to as any kind of fraudulent activities that are executed by fraudulent users either a human or an automated script. The combat between researchers and fraudulent users never ends because more smarter strategies are being used by the fraudsters to bypass the significant number of detection and prevention solutions. The Real Time Mobile Ad Investigator-RTMAI is proposed as a software solution to address this problem where a novel supervised learning algorithm based on the hidden Markov model along with a rule engine have been proposed to classify fraudulent impressions in real time. Furthermore, RTMAI proposed a solution to address the class imbalance problem which is generic to most of the classification datasets. The experimental results show the significance of the proposed approach to classify the fraud or non-fraud clicks/events, impressions and even user sessions more confidently in real time. [ABSTRACT FROM AUTHOR]

Click Fraud is a fraudulent act of clicking on pay-per-click advertisements to increase the site’s revenue or to drain revenue from the advertiser. This illegal act has been putting commercial industries in a dilemma for quite some time. These industries think twice before advertising their products on websites and mobile-apps, as many parties try to exploit them. To safely promote their products, there must be an efficient system to detect click fraud. To address this problem, we propose a model called CFXGB (Cascaded Forest and XGBoost). The proposed model, classified under supervised machine learning, is a combination of two learning models used for feature transformation and classification. We showcase its superior performance compared to other related models, and make a comparison with multiple click fraud datasets with varying sizes.

The absence of a publicly available user-click dataset makes the task of fraudster identification particularly challenging to detect click fraud in online advertising. However, the task becomes more complicated with concept drift, where a publisher appears in distinct sets with the same pattern but differs in real status labels. Fraud-detection algorithms developed the actual status labels. The reliability of the predictions made by learning models needs to be examined to deal with the concept drift concerning the changing behaviour of fraudulent publishers without re-training the predictive model from scratch. However, the scarcity of pre-trained models aggravates the issue. Thus, we proposed a 1D-convolutional neural network-based fraudsters identification network (FINet) that deals with such challenges using a model trained on a correlated prediction modelling problem using transfer learning. To overcome the scarcity of pre-trained models, we designed a model FINet trained on the correlated dataset TalkingData Ad Tracking Fraud Detection (TDA) and utilized the weights of the trained model to learn a model on the FDMA2012 dataset better. This predicted modelling on a distinct but related problem is re-used for accelerating the training and improving the FINet's fraudster identification performance. The proposed FINet's behaviour is investigated based on eight optimizer algorithms in terms of Average Precision, Recall, F1-score, and AUC scores and performance generalization is verified by conducting experiments with existing state-of-art deep learning models. The implementation results are notably higher than the existing state-of-the-art deep learning models and exhibit effective performance towards fraudster's identification in detecting click fraud. [ABSTRACT FROM AUTHOR]

This paper proposed a promising new method for detecting a fraudulent group.First, it used the frequent itemsets mining algorithm to reveal individual member with joint actions on advertisement click as a suspicious group.Next, based on properties analysis on click behavior of members of the suspicious group, it used outlier detection method to distinguish those whose behavior was significantly different with others, as suspicious targets.Finally, it detected the real fraud group and fraudulent users after applying Bayesian classifier on all the suspicious fraudulent users.Experimental results indicate the feasibility and validity of this method, which is based on real dataset.It gives a new approach to the detection of fraudulent clicks. [ABSTRACT FROM AUTHOR]

Smartphone advertisement is increasingly used among many applications and allows developers to obtain revenue through in-app advertising. Our study aims at identifying potential security risks of mobile-based advertising services where advertisers are charged for their advertisements on mobile applications. In the Android platform, we particularly implement bot programs that can massively generate click events on advertisements on mobile applications and test their feasibility with eight popular advertising networks. Our experimental results show that six advertising networks (75 %) out of eight are vulnerable to our attacks. To mitigate click fraud attacks, we suggest three possible defense mechanisms: (1) filtering out program-generated touch events; (2) identifying click fraud attacks with faked advertisement banners; and (3) detecting anomalous behaviors generated by click fraud attacks. We also discuss why few companies were only willing to deploy such defense mechanisms by examining economic misincentives on the mobile advertising industry. [ABSTRACT FROM AUTHOR]

Pay-per-click (PPC) is a common pricing model used to pay for ads on the Web and is open to the possibility for click fraud, where clicks are not from a legitimate user. Identifying click fraud is generally done in a three-stage process: the service provider (SP) first classifies clicks as fraudulent or not, then the advertiser does the same with a different technology, and if there is a disagreement, the SP examines further and his conclusions are considered binding. The advertiser pays for clicks that are identified as valid in the first two stages or confirmed as valid in the last stage. We model the choice of the identification technologies as a double moral hazard problem. We analyze the case where the PPC is incentive compatible to overcome the moral hazard problem, and examine the question of whether the incentive compatible PPC is sufficient to incentivize the two parties to unilaterally make further improvements to their identification technologies and simultaneously increase their profits. We show that when the cost of the third-stage identification technology is large, which is likely to be the case because of its complexity and use of expensive human experts, the incentive compatible PPC does not support unilateral technological improvements. We then examine a setting where the third-stage identification is delegated to a third party and find that this arrangement can induce unilateral improvements to the identification technologies in the first two stages. Collectively our results show that although the PPC model itself may not induce improvements in the first two stages of click fraud identification, a common arrangement espoused of having a third party resolve disagreements helps make PPC support unilateral technological improvements. Accordingly, we show an indirect benefit to the third-party arrangement. [ABSTRACT FROM AUTHOR]