Exposing click-fraud using a burst detection algorithm

Antoniou, Dimitri; Paschou, Mersini; Sakkopoulos, Evangelos; Sourla, Efrosini; Tzimas, Giannis; Tsakalidis, A.; Viennas, Emmanouil

doi:10.1109/iscc.2011.5983854

Cited by 8 publications

(4 citation statements)

References 9 publications

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“…There are various techniques that can be employed in order to detect click fraud, which are mainly based on the application of data mining and machine learning techniques in order to provide detailed evidence of possible click fraud patterns. The work of (Antoniou et al, 2011) is the most resembling to ours in the problem. In this paper, an approach for click fraud detection was proposed, that analyzed the number of visits during a certain time interval, depending on the kind of the investigated click fraud and on the application at hand (e.g.…”

Section: Related Workmentioning

confidence: 66%

“…We should mention that in the algorithm described in (Antoniou et al, 2011) a splay tree was used in order to store Web pages and a stack of timestamps for each node of the splay tree. In our present approach, we simplify the employed data structures and we save time and space, since we have replaced the splay tree with a simple two dimensional array, while additionally by using the two dimensional array we are no longer in need of the stacks storing the timestamps of the various users' accesses.…”

Section: Description Of the Algorithmmentioning

confidence: 99%

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Designing a Click Fraud Detection Algorithm - Exposing Suspect Networks

Antoniou¹,

Makris²,

Meridou³

et al. 2013

Proceedings of the 9th International Conference on Web Information Systems and Technologies

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As the use of the Web expands and appears almost everywhere in business' practices, new algorithmic problems appear and need to be efficiently handled; one of them that has attracted the attention of both researchers and practitioners is click fraud. Click fraud can be defined as the practice of repetitively clicking on search ads without being actually interested in the content of the related links, with the intention of either increasing the Website's profits or exhausting an advertiser's budget. In this work, we propose an algorithm, which exposes suspect networks instead of single IPs, based on utilizing efficient data structures that have not been employed in previous works.

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Section: Related Workmentioning

confidence: 66%

Section: Description Of the Algorithmmentioning

confidence: 99%

Designing a Click Fraud Detection Algorithm - Exposing Suspect Networks

Antoniou¹,

Makris²,

Meridou³

et al. 2013

Proceedings of the 9th International Conference on Web Information Systems and Technologies

View full text Add to dashboard Cite

show abstract

“…In [11], they used Social Network Analysis to ind top three ad networks that were being used to spread the fraud click malware. In [1], they use Splay trees to store the IPs via which fraud clicks occur based on a burst. In [22], they irst ind the eigenvalues of displayed ad images, if the ad is shown it is attested, based on if the eigenvalues of the image match those stored in their server, the user is certiied as honest, if not then it is analysed.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning-based Model to Fight Against Ad Click Fraud

Thejas

Boroojeni

Chandna

et al. 2019

Proceedings of the 2019 ACM Southeast Conference

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Click fraud is a fast-growing cyber-criminal activity with the aim of deceptively clicking on the advertisements to make the proit to the publisher or cause loss to the advertiser. Due to the popularity of smartphones since the last decade, most of the modern-day advertisement businesses have been shifting their focus toward mobile platforms. Nowadays, in-app advertisement on mobile platforms is the most targeted victim of click fraud. Malicious entities launch attacks by clicking ads to artiicially increase the click rates of speciic ads without the intention of using them for legitimate purposes. The fraud clicks are supposed to be caught by the ad providers as part of their service to the advertisers; however, there is a lack of research in the current literature for addressing and evaluating different techniques of click fraud detection and prevention. Another challenge toward click fraud detection is that the attack model can itself be an active learning system (smart attacker) with the aim of actively misleading the training process of fraud detection model via polluting the training data. In this paper, we propose a deep-learning based model to address the challenges as mentioned above. The model is a hybrid of artiicial neural network (ANN), auto-encoder and semi-supervised generative adversarial network (GAN). Our proposed approach triumphs excellent accuracy than other models. CCS CONCEPTS· Information systems → Online advertising; · Security and privacy → Intrusion/anomaly detection and malware mitigation; · Computing methodologies → Machine learning.

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“…is CAP model may incentivize malicious mobile content publishers (typically app owners) to generate fraudulent actions on advertisements to get more financial returns [1][2][3]. Some traditional methods and techniques have been used for detecting and stopping click fraud, such as threshold-based method [4], CAPTCHA [5], splay tree [6], TrustZone [7], power spectral density analysis [8], and social network analysis [9].…”

Section: Introductionmentioning

confidence: 99%

GFD: A Weighted Heterogeneous Graph Embedding Based Approach for Fraud Detection in Mobile Advertising

Zhuang

et al. 2020

Security and Communication Networks

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Online mobile advertising plays a vital role in the mobile app ecosystem. The mobile advertising frauds caused by fraudulent clicks or other actions on advertisements are considered one of the most critical issues in mobile advertising systems. To combat the evolving mobile advertising frauds, machine learning methods have been successfully applied to identify advertising frauds in tabular data, distinguishing suspicious advertising fraud operation from normal one. However, such approaches may suffer from labor-intensive feature engineering and robustness of the detection algorithms, since the online advertising big data and complex fraudulent advertising actions generated by malicious codes, botnets, and click-firms are constantly changing. In this paper, we propose a novel weighted heterogeneous graph embedding and deep learning-based fraud detection approach, namely, GFD, to identify fraudulent apps for mobile advertising. In the proposed GFD approach, (i) we construct a weighted heterogeneous graph to represent behavior patterns between users, mobile apps, and mobile ads and design a weighted metapath to vector algorithm to learn node representations (graph-based features) from the graph; (ii) we use a time window based statistical analysis method to extract intrinsic features (attribute-based features) from the tabular sample data; (iii) we propose a hybrid neural network to fuse graph-based features and attribute-based features for classifying the fraudulent apps from normal apps. The GFD approach was applied on a large real-world mobile advertising dataset, and experiment results demonstrate that the approach significantly outperforms well-known learning methods.

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Exposing click-fraud using a burst detection algorithm

Cited by 8 publications

References 9 publications

Designing a Click Fraud Detection Algorithm - Exposing Suspect Networks

Designing a Click Fraud Detection Algorithm - Exposing Suspect Networks

Deep Learning-based Model to Fight Against Ad Click Fraud

GFD: A Weighted Heterogeneous Graph Embedding Based Approach for Fraud Detection in Mobile Advertising

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