ConOut: Contextual Outlier Detection with Multiple Contexts: Application to Ad Fraud

Meghanath, Meghanath Macha; Pai, Deepak; Akoglu, Leman

doi:10.1007/978-3-030-10925-7_9

Cited by 5 publications

(4 citation statements)

References 12 publications

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“…Different from the above graph-based and pattern-based analysis schemes, three contextual-based attributes concerning interarrival time (IAT), diurnal activity (DA), and eigenscore (ES) were analyzed in comparison-shopping services to calculate a click's credible score for detecting whether it is fake in [26]. Moreover, Meghanath et al [28] proposed a new contextual outlier detection technology (ConOut) and applied it to the advertising domain to identify fraudulent publishers. Besides, the work in [30] presents Bag-of-Words algorithm to assess clicks in online advertising system, which is based on the concept of text search methods.…”

Section: Statistical Analysis-based Schemesmentioning

confidence: 99%

“…Besides, the work in [30] presents Bag-of-Words algorithm to assess clicks in online advertising system, which is based on the concept of text search methods. However, the outlier detection technology in [28] and the Bag-of-Words algorithm in [30] involve users' real username and address of the visitor, which reveals user's identity privacy.…”

Section: Statistical Analysis-based Schemesmentioning

confidence: 99%

“…However, the dataset used for training is easy to be mixed with fraudulent clicks in these schemes, causing the process of training a model to be susceptible to adversarial attack [23]. ence, statistical analysis-based schemes [1,10,[24][25][26][27][28][29][30][31][32] aim to mitigate the adversarial attacks. For instance, the schemes in [10,25] predict malicious crowdsourcing platforms by clustering algorithms.…”

Section: Introductionmentioning

confidence: 99%

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BCFDPS: A Blockchain-Based Click Fraud Detection and Prevention Scheme for Online Advertising

Lyu

Zhou

et al. 2022

Security and Communication Networks

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Online advertising, which depends on consumers’ click, creates revenue for media sites, publishers, and advertisers. However, click fraud by criminals, i.e., the ad is clicked either by malicious machines or hiring people, threatens this advertising system. To solve the problem, many schemes are proposed which are mainly based on machine learning or statistical analysis. Although these schemes mitigate the problem of click fraud, several problems still exist. For example, some fraudulent clicks are still in the wild since their schemes only discover the fraudulent clicks with a probability approaching but not 100%. Also, the process of detecting a click fraud is executed by a single publisher, which makes a chance for the publisher to obtain illegal income by deceiving advertisers and media sites. Besides, the identity privacy of consumers is also exposed because the schemes deal with the plain text of consumers’ real identity. Therefore, in this paper, a blockchain-based click fraud detection and prevention scheme (BCFDPS) for online advertising is proposed to deal with the above problems. Specifically, the BCFDPS mainly introduces bilinear pairing to implicitly verify whether a consumer’s real digital identity is contained in a click message to significantly avoid click fraud and employs a consortium blockchain to ensure the transparency of the detection and prevention process. In our scheme, the clicks by machines or fraud ones by a human can be accurately detected and prevented by media sites, publishers, and advertisers. Furthermore, ciphertext-policy attribute-based encryption is adopted to protect the identity privacy of consumers. The implementation and evaluation results show that compared with the existing click fraud detection and prevention schemes based on machine learning and statistical analysis, BCFDPS achieves detection of each fraudulent click with a probability of 100% and consumes lower computation cost; furthermore, BCFDPS adds functions of consumers’ privacy protection and click fraud detection and prevention, compared to the existing blockchain-based online advertising scheme, by introducing limited communication cost ( 4,984 bytes) at lower storage cost.

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Section: Statistical Analysis-based Schemesmentioning

confidence: 99%

Section: Statistical Analysis-based Schemesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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BCFDPS: A Blockchain-Based Click Fraud Detection and Prevention Scheme for Online Advertising

Lyu

Zhou

et al. 2022

Security and Communication Networks

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“…Moreover, Zheng et al (2017) apply robust metric learning on contextual features to find more meaningful contextual neighbours and then leverage k-NN kernel regression to predict the behavioral feature values. Meghanath et al (2018) develop ConOut to automatically find and incorporate multiple contexts to identify and interpret outliers.…”

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confidence: 99%

Explainable contextual anomaly detection using quantile regression forests

Leeuwen

2023

Data Min Knowl Disc

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Traditional anomaly detection methods aim to identify objects that deviate from most other objects by treating all features equally. In contrast, contextual anomaly detection methods aim to detect objects that deviate from other objects within a context of similar objects by dividing the features into contextual features and behavioral features. In this paper, we develop connections between dependency-based traditional anomaly detection methods and contextual anomaly detection methods. Based on resulting insights, we propose a novel approach to inherently interpretable contextual anomaly detection that uses Quantile Regression Forests to model dependencies between features. Extensive experiments on various synthetic and real-world datasets demonstrate that our method outperforms state-of-the-art anomaly detection methods in identifying contextual anomalies in terms of accuracy and interpretability.

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Wisdom of the contexts: active ensemble learning for contextual anomaly detection

Calikus

Nowaczyk

Bouguelia

et al. 2022

Data Min Knowl Disc

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In contextual anomaly detection, an object is only considered anomalous within a specific context. Most existing methods use a single context based on a set of user-specified contextual features. However, identifying the right context can be very challenging in practice, especially in datasets with a large number of attributes. Furthermore, in real-world systems, there might be multiple anomalies that occur in different contexts and, therefore, require a combination of several “useful” contexts to unveil them. In this work, we propose a novel approach, called wisdom of the contexts (WisCon), to effectively detect complex contextual anomalies in situations where the true contextual and behavioral attributes are unknown. Our method constructs an ensemble of multiple contexts, with varying importance scores, based on the assumption that not all useful contexts are equally so. We estimate the importance of each context using an active learning approach with a novel query strategy. Experiments show that WisCon significantly outperforms existing baselines in different categories (i.e., active learning methods, unsupervised contextual and non-contextual anomaly detectors) on 18 datasets. Furthermore, the results support our initial hypothesis that there is no single perfect context that successfully uncovers all kinds of contextual anomalies, and leveraging the “wisdom” of multiple contexts is necessary.

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ConOut: Contextual Outlier Detection with Multiple Contexts: Application to Ad Fraud

Cited by 5 publications

References 12 publications

BCFDPS: A Blockchain-Based Click Fraud Detection and Prevention Scheme for Online Advertising

BCFDPS: A Blockchain-Based Click Fraud Detection and Prevention Scheme for Online Advertising

Explainable contextual anomaly detection using quantile regression forests

Wisdom of the contexts: active ensemble learning for contextual anomaly detection

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