AdAttester

Li, Wenhao; Li, Haibo; Chen, Haibo; Xia, Yubin

doi:10.1145/2742647.2742676

Cited by 67 publications

(2 citation statements)

References 28 publications

(20 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Santos et al [75] use TrustZone to build a trusted-language runtime to protect the confidentiality and integrity of .NET mobile applications running in the normal world. Li et al [76] propose a verifiable mobile advertisement framework to detect and prevent advertisement frauds using TrustZone. Marforio et al [77] propose a location-based secondfactor authentication mechanisms for payment at pointof-sale.…”

Section: Related Workmentioning

confidence: 99%

SecurePay: Strengthening Two-Factor Authentication for Arbitrary Transactions

Konoth

Fischer

Fokkink

et al. 2020

2020 IEEE European Symposium on Security and Privacy (EuroS&P)

View full text Add to dashboard Cite

General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.• Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal Take down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.

show abstract

Section: Related Workmentioning

confidence: 99%

SecurePay: Strengthening Two-Factor Authentication for Arbitrary Transactions

Konoth

Fischer

Fokkink

et al. 2020

2020 IEEE European Symposium on Security and Privacy (EuroS&P)

View full text Add to dashboard Cite

show abstract

“…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. Researchers build on several published theoretical results to devise the Similarity-Seeker algorithm that discovers coalitions made by pairs of fraudsters [24].…”

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

View full text Add to dashboard Cite

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.

show abstract