Relevant bibliographies by topics / Ad fraud detection

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  3. Conference papers

Academic literature on the topic 'Ad fraud detection'

Author: Grafiati
Published: 7 June 2025
Last updated: 16 July 2025

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Journal articles on the topic "Ad fraud detection"

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Akash Vir and Dr. Shivam Upadhyay. "Smart Guard: A Comprehensive Approach to Ad Click Fraud Detection." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 10, no. 6 (2024): 2018–24. https://doi.org/10.32628/cseit2410612403.

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Digital advertising is plagued by ad click fraud, which can result in large financial losses and skewed statistics. Current research on ad click fraud detection is summarized in this systematic review, which also assesses different methods and approaches. The review addresses difficulties, outlines the efficacy of various approaches, and makes recommendations for future lines of inquiry. Ad click fraud is the practice of creating phony clicks on internet ads, which can be done by malevolent humans, click farms, or automated bots. Click metrics are inflated by this fake activity, resulting in false performance statistics and squandered advertising budgets. Ad click fraud must be identified and stopped in order to preserve the efficacy and integrity of digital advertising campaigns. The objectives of this review are to list popular methods for detecting ad click fraud, assess how well they work in practical situations, talk about the drawbacks and restrictions of the approaches currently in use, and recommend future lines of inquiry to improve ad click fraud detection. The analysis concluded that when it comes to identifying ad click fraud, machine learning and artificial intelligence (AI) algorithms typically perform better than rule-based approaches. However, the caliber and variety of the training data determine how effective these methods are. The results emphasize how crucial it is to fight ad click fraud by utilizing sophisticated detection methods. To increase detection accuracy and lower financial losses, advertisers should spend money on AI and machine learning-based solutions. To keep up with changing fraud strategies, future research should concentrate on hybrid techniques, real-time detection, and cross-platform analysis.
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Almeida, Paulo S., and João J. C. Gondim. "Click Fraud Detection and Prevention System for Ad Networks." Journal of Information Security and Cryptography (Enigma) 5, no. 1 (2019): 27. http://dx.doi.org/10.17648/jisc.v5i1.71.

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Click fraud detection consists of identifying the intention behind received clicks, given only technical data and context information. Reviewing concepts involved in click fraud practices and related work, a system that detects and prevents this type of fraud is proposed and implemented. The system is based and implemented on an ad network, one of the 3 main agents in the online ad environment, and for its validation, 3 servers were used, representing the publisher, the ad network with the system implemented and the announcer, and a bot that attempts to commit a click fraud.
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Neeraja, Anupam, Sriram, Subhani Shaik, and V. Kakulapati. "Fraud Detection of AD Clicks Using Machine Learning Techniques." Journal of Scientific Research and Reports 29, no. 7 (2023): 84–89. http://dx.doi.org/10.9734/jsrr/2023/v29i71762.

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Although all businesses face the possibility of fraud, those that rely on internet advertising face an especially high risk of click fraud, which may lead to inaccurate click statistics and unnecessary expenditures. The cost per click for advertising channels might skyrocket if enough people click on the ads. Internet advertising is becoming a significant revenue source for many websites. Under this model, advertisers pay the publisher a flat rate for each click-through from the ad to the advertiser's site. Since spending much on Internet advertising requires significant resources, the term "click fraud" refers to an attack tactic in which the perpetrator repeatedly clicks on a single link for the sole purpose of generating illicit revenue. By clicking on a pay-per-click (PPC) ad many times using a script, fraudsters may trick online advertisers into paying for clicks that never happened. We may use a variety of methods to identify click fraud anytime a human or computer program clicks on a particular link, and then use the click-through rate to ascertain whether the clicker is legitimate. This work provides a machine-learning strategy for predicting user click fraud, which will enable us to distinguish between fraudulent and legitimate clicks and, therefore, identify fraudulent users from legitimate ones. We have used KNN, SVC, and Random Forest models for this purpose.
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Bathula, Mahesh, Rama Chaithanya Tanguturi, and Srinivasa Rao Madala. "Click Fraud Detection Approaches to analyze the Ad Clicks Performed by Malicious Code." Journal of Physics: Conference Series 2089, no. 1 (2021): 012077. http://dx.doi.org/10.1088/1742-6596/2089/1/012077.

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Abstract Mobile PR is an important component of the mobile app ecosystem. A major threat to this ecosystem’s long-term health is click fraud, which involves clicking on ads while infected with malware or using an automated bot to do it for you. The methods used to identify click fraud now focus on looking at server requests. Although these methods have the potential to produce huge numbers of false negatives, they may easily be avoided if clicks are hidden behind proxies or distributed globally. AdSherlock is a customer-side (inside the app) efficient and deployable click fraud detection system for mobile applications that we provide in this work. AdSherlock separates the computationally expensive click request identification procedures into an offline and online approach. AdSherlock uses URL (Uniform Resource Locator) tokenization in the Offline phase to create accurate and probabilistic patterns. These models are used to identify click requests online, and an ad request tree model is used to detect click fraud after that. In order to develop and evaluate the AdSherlock prototype, we utilise actual applications. It injects the online detector directly into an executable software package using binary instrumentation technology (BIT). The findings show that AdSherlock outperforms current state-of-the-art methods for detecting click fraud with little false positives. Advertisement requests identification, mobile advertising fraud detection are some of the keywords used in this article.
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Alzahrani, Reem A., and Malak Aljabri. "AI-based Techniques for Ad Click Fraud Detection and Prevention: Review and Research Directions." Journal of Sensor and Actuator Networks 12, no. 1 (2022): 4. http://dx.doi.org/10.3390/jsan12010004.

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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.
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Sadeghpour, Shadi, and Natalija Vlajic. "Ads and Fraud: A Comprehensive Survey of Fraud in Online Advertising." Journal of Cybersecurity and Privacy 1, no. 4 (2021): 804–32. http://dx.doi.org/10.3390/jcp1040039.

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Over the last two decades, we have witnessed a fundamental transformation of the advertising industry, which has been steadily moving away from the traditional advertising mediums, such as television or direct marketing, towards digital-centric and internet-based platforms. Unfortunately, due to its large-scale adoption and significant revenue potential, digital advertising has become a very attractive and frequent target for numerous cybercriminal groups. The goal of this study is to provide a consolidated view of different categories of threats in the online advertising ecosystems. We begin by introducing the main elements of an online ad platform and its different architecture and revenue models. We then review different categories of ad fraud and present a taxonomy of known attacks on an online advertising system. Finally, we provide a comprehensive overview of methods and techniques for the detection and prevention of fraudulent practices within those system—both from the scientific as well as the industry perspective. The main novelty of our work lies in the development of an innovative taxonomy of different types of digital advertising fraud based on their actual executors and victims. We have placed different advertising fraud scenarios into real-world context and provided illustrative examples thereby offering an important practical perspective that is very much missing in the current literature.
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Gabryel, Marcin, Magdalena M. Scherer, Łukasz Sułkowski, and Robertas Damaševičius. "Decision Making Support System for Managing Advertisers By Ad Fraud Detection." Journal of Artificial Intelligence and Soft Computing Research 11, no. 4 (2021): 331–39. http://dx.doi.org/10.2478/jaiscr-2021-0020.

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Abstract Efficient lead management allows substantially enhancing online channel marketing programs. In the paper, we classify website traffic into human- and bot-origin ones. We use feedforward neural networks with embedding layers. Moreover, we use one-hot encoding for categorical data. The data of mouse clicks come from seven large retail stores and the data of lead classification from three financial institutions. The data are collected by a JavaScript code embedded into HTML pages. The three proposed models achieved relatively high accuracy in detecting artificially generated traffic.
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Sadeghpour, Shadi, and Natalija Vlajic. "Click Fraud in Digital Advertising: A Comprehensive Survey." Computers 10, no. 12 (2021): 164. http://dx.doi.org/10.3390/computers10120164.

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Recent research has revealed an alarming prevalence of click fraud in online advertising systems. In this article, we present a comprehensive study on the usage and impact of bots in performing click fraud in the realm of digital advertising. Specifically, we first provide an in-depth investigation of different known categories of Web-bots along with their malicious activities and associated threats. We then ask a series of questions to distinguish between the important behavioral characteristics of bots versus humans in conducting click fraud within modern-day ad platforms. Subsequently, we provide an overview of the current detection and threat mitigation strategies pertaining to click fraud as discussed in the literature, and we categorize the surveyed techniques based on which specific actors within a digital advertising system are most likely to deploy them. We also offer insights into some of the best-known real-world click bots and their respective ad fraud campaigns observed to date. According to our knowledge, this paper is the most comprehensive research study of its kind, as it examines the problem of click fraud both from a theoretical as well as practical perspective.
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Lyu, Qiuyun, Hao Li, Renjie Zhou, Jilin Zhang, Nailiang Zhao, and Yan Liu. "BCFDPS: A Blockchain-Based Click Fraud Detection and Prevention Scheme for Online Advertising." Security and Communication Networks 2022 (April 29, 2022): 1–20. http://dx.doi.org/10.1155/2022/3043489.

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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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Keserwani, Pankaj Kumar, Mahesh Chandra Govil, and Emmanuel Shubhakar Pilli. "The web ad-click fraud detection approach for supporting to the online advertising system." International Journal of Swarm Intelligence 7, no. 1 (2022): 3. http://dx.doi.org/10.1504/ijsi.2022.121091.

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Book chapters on the topic "Ad fraud detection"

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Thimonier, Hugo, Fabrice Popineau, Arpad Rimmel, Bich-Liên Doan, and Fabrice Daniel. "Comparative Evaluation of Anomaly Detection Methods for Fraud Detection in Online Credit Card Payments." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-4581-4_4.

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AbstractThis study explores the application of anomaly detection (AD) methods in imbalanced learning tasks, focusing on fraud detection using real online credit card payment data. We assess the performance of several recent AD methods and compare their effectiveness against standard supervised learning methods. Offering evidence of distribution shift within our dataset, we analyze its impact on the tested models’ performances. Our findings reveal that LightGBM exhibits significantly superior performance across all evaluated metrics but suffers more from distribution shifts than AD methods. Furthermore, our investigation reveals that LightGBM also captures the majority of frauds detected by AD methods. This observation challenges the potential benefits of ensemble methods to combine supervised, and AD approaches to enhance performance. In summary, this research provides practical insights into the utility of these techniques in real-world scenarios, showing LightGBM’s superiority in fraud detection while highlighting challenges related to distribution shifts.
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Zhu, Xingquan, Haicheng Tao, Zhiang Wu, Jie Cao, Kristopher Kalish, and Jeremy Kayne. "Ad Fraud Categorization and Detection Methods." In Fraud Prevention in Online Digital Advertising. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-56793-8_4.

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Zhu, Xingquan, Haicheng Tao, Zhiang Wu, Jie Cao, Kristopher Kalish, and Jeremy Kayne. "Ad Fraud Detection Tools and Systems." In Fraud Prevention in Online Digital Advertising. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-56793-8_6.

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Gohil, Nayanaba Pravinsinh, and Arvind D. Meniya. "Click Ad Fraud Detection Using XGBoost Gradient Boosting Algorithm." In Communications in Computer and Information Science. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-76776-1_5.

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Meghanath, M. Y., Deepak Pai, and Leman Akoglu. "ConOut: Contextual Outlier Detection with Multiple Contexts: Application to Ad Fraud." In Machine Learning and Knowledge Discovery in Databases. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-10925-7_9.

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Srivastav, Anurag, and Laxmi Ahuja. "An Exhaustive Review on Detecting Online Click-Ad Frauds." In Innovations in Computer Science and Engineering. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2043-3_27.

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Naumenko, Maksym, Iryna Hrashchenko, Tetiana Tsalko, Svitlana Nevmerzhytska, Svitlana Krasniuk, and Yurii Kulynych. "Innovative technological modes of data mining and modelling for adaptive project management of food industry competitive enterprises in crisis conditions." In PROJECT MANAGEMENT: INDUSTRY SPECIFICS. TECHNOLOGY CENTER PC, 2024. https://doi.org/10.15587/978-617-8360-03-0.ch2.

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Developed in this research scientific and practical applied project solutions regarding Data Mining for enterprises and companies (on the example of food industry) involve the application of advanced cybernetic computing methods/algorithms, technological modes and scenarios (for integration, pre-processing, machine learning, testing and in-depth comprehensive interpretation of the results) of analysis and analytics of large structured and semi-structured data sets for training high-quality descriptive, predictive and even prescriptive models. The proposed by authors multi-mode adaptive Data Mining synergistically combines in parallel and sequential scenarios: methods of preliminary EDA, statistical analysis methods, business intelligence methods, classical machine learning algorithms and architectures, advanced methods of testing and verification of the obtained results, methods of interdisciplinary empirical expert interpretation of results, knowledge engineering formats/techniques – for discovery/detection previously unknown, hidden and potentially useful patterns, relationships and trends (for innovative project management). The main methodological and technological goal of this developed methodology of multi-mode adaptive Data Mining for food industry enterprises is to increase the completeness (support) and accuracy of business and technical-technological modeling on all levels of project management of food industry enterprises: strategic, tactical and operational. By optimally configuring hyperparameters, parameters, algorithms/methods and architecture of multi-target and multidimensional explicit and implicit descriptive and predicative models, using high-performance hybrid parallel soft computing for machine learning – the improved methodology of multimode Data Mining (proposed by the authors) allows to find/detect/mine for new, useful, hidden corporate knowledge from previously collected, extracted, integrated Data Lakes, stimulating the overall efficiency, sustainability, and therefore competitiveness, of food industry enterprises at various organizational scales (from individual, craft productions to integrated international holdings) and in various food product groups and niches. In more detail, the purposes of this research are revealed in two meaningful modules: The first part of the detailed goals and objectives of this research relate to the effective use of Data Mining (and modeling) in the competitive management of enterprises and companies in modern economy, namely: – research and verification of the effectiveness of the basic/main three types of Data Mining in the management of a competitive enterprise; – detection of basic/main difficulties and challenges of Data Mining technology in the management of a competitive enterprise; – research and generation of a list of basic/main expedient functional applied corporate tasks for the application of the improved concept of Data Mining; – determination of the list of basic/main results of using the proposed Data Mining concept and methodology for an effective and competitive enterprise in dynamic and crisis conditions; – finding the basic/main advantages of using the proposed Data Mining concept and methodology for an effective and competitive enterprise in dynamic and crisis conditions: – research of the basic/main technological problems of using the proposed concept and methodology of Data Mining for an effective and competitive enterprise in dynamic and crisis conditions; – detection of the basic/main ethical problems of using the proposed Data Mining concept and methodology for an effective and competitive enterprise in dynamic and crisis conditions; – research and search for basic/main perspectives of intelligent data analysis in the management of a competitive enterprise or company. 2. The second and main part of the detailed goals and objectives of this publication relate to the effective use of Data Mining (and modeling) in the competitive management of enterprises and companies in the food industry, namely: – determination of features and methods of analysis and analytics of High Dimensional big data of at enterprises of the food industry; – research of features and development of methodological and technological techniques for effective mode of OnLine Data Mining at food industry enterprises; – research of specifics and development of recommendations regarding the effective mode of Ad-Hoc Data Mining at food industry enterprises; – research of the specifics and development of applied recommendations regarding the effective mode of Anomaly & Fraud Detection of technological data of food industry enterprises; – identification of directions and development of recommendations for effective use of Hybrid Data Mining at food industry enterprises; – detection of features and development of a complex of scientific and practical recommendations regarding the effective regime of Crisis Data Mining at food industry enterprises in dynamic and unstable external conditions; – identification of directions and development of recommendations for future trends in the effective use of Data Mining at food industry enterprises. It can not be argued that in modern conditions (pre-crisis, crisis and post-crisis conditions of both regional food industries and the global world; globalization and simultaneous very narrow specialization of the food industry sectors; the need to take into account a huge amount of stream and packet information from various sources and various formats; the need for a quick adaptive optimal management response/adaptation in response to rapid changes in the global or regional market situation; unstable and difficult to predict dynamics of external influences: international, national, sectoral, local direct regulatory and indirect public regulation of the food industry) – deployment of the multi-mode adaptive Data Mining methodology proposed by the authors – will result in enterprises, companies and organizations/institutions of the food industry gaining additional competitive advantages at the state, regional, branch and corporate management levels.
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Sağlam, Munise Hayrun, and Ibrahim Kirçova. "The Role of Artificial Intelligence in Ad Fraud Detection in the Blockchain and Programmatic Advertising Ecosystem." In Advances in Marketing, Customer Relationship Management, and E-Services. IGI Global, 2025. https://doi.org/10.4018/979-8-3693-7041-4.ch003.

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The role of artificial intelligence (AI) in ad fraud detection within the blockchain and programmatic advertising (PA) ecosystem is becoming increasingly crucial. As digital advertising continues to evolve, the complexities and scale of real-time transactions pose significant challenges for advertisers and publishers. Ad fraud, including click fraud, results in substantial financial losses, projected to reach $172 billion by 2028. AI technologies, such as machine learning and deep learning, have proven effective in identifying fraudulent activities and enhancing the accuracy and reliability of ad campaigns. Additionally, blockchain technology offers transparency and security by recording and verifying each ad interaction, ensuring data integrity and trust. This book chapter explores the significance of AI and blockchain in addressing ad fraud, highlighting their potential to transform the digital advertising landscape and improve the efficiency and effectiveness of advertising investments.
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Baranidharan, S., David Winster, K. Dhanalakshmi, and R. Rajkumar. "Combating Evolving Threats." In Advances in Marketing, Customer Relationship Management, and E-Services. IGI Global, 2025. https://doi.org/10.4018/979-8-3693-7041-4.ch005.

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This systematic review investigates online ad fraud detection and brand safety research from 2011 to 2024. The analysis reveals a continuous battle against click fraud and its evolving forms. Machine learning has become a cornerstone of detection efforts, offering superior capabilities compared to traditional methods. The rise of mobile advertising necessitated the development of specialized solutions to address distinct user behavior and data patterns on this platform. However, research highlights an expanding threat landscape beyond click fraud, encompassing impression fraud and placement fraud. Brand safety concerns have also gained prominence, emphasizing the importance of protecting brand reputation. The review underscores the need for collaboration between researchers and industry professionals to achieve a more secure and trustworthy online advertising ecosystem.
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Singh, Bhupinder, Anjali Raghav, Saquib Ahmed, Manmeet Kaur Arora, and Sahil Lal. "Smearing Machine Learning and Deep Learning in E-Commerce Transactions for Monetary Justice." In Advances in Computational Intelligence and Robotics. IGI Global, 2025. https://doi.org/10.4018/979-8-3693-9395-6.ch014.

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Machine learning and deep learning techniques have emerged as powerful tools against several types of financial fraud. The banking and financial industry, a fundamental component of contemporary economies, is experiencing a significant upheaval due to the rise of digital transactions. This transition has resulted in an increase in financial fraud, necessitating a fundamental change in security standards. The use of advanced analytics, such as anomaly detection and pattern recognition, is examined to establish a strong defense against the continually changing strategies used by fraudulent entities in the ad click sector. Credit card management, a constant target for nefarious actions, necessitates an advanced strategy for fraud detection. This chapter examines AI-based document verification systems, highlighting their crucial role in safeguarding transactions reliant on document authentication. It addresses issues related to falsified documentation through novel methods, including the integration of blockchain technology with AI.
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Conference papers on the topic "Ad fraud detection"

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Almeida, Paulo S. de, and João J. C. Gondim. "Click Fraud Detection and Prevention System For Ad Networks." In Anais Estendidos do Simpósio Brasileiro de Segurança da Informação e de Sistemas Computacionais. Sociedade Brasileira de Computação - SBC, 2018. http://dx.doi.org/10.5753/sbseg_estendido.2018.4157.

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Click fraud detection consists of identifying the intention behind the clicks a system receives. A system that detects and prevents this type of fraud is proposed and implemented, based on the ad network, one of the 3 agents in the online ad environment. To validate, 3 servers were used, representing said agents. A bot simulates an attacker, and the frauds are ultimately identified by our proposed detector in tested scenarios.
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Dong, Feng, Haoyu Wang, Li Li, et al. "FraudDroid: automated ad fraud detection for Android apps." In ESEC/FSE '18: 26th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering. ACM, 2018. http://dx.doi.org/10.1145/3236024.3236045.

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Justin Sophia., I., R. Meganathan, B. Dhanasakkaravarthi, S. Satheesh Kumar, and Abhishek Mishra. "Accurate Click Fraud Rapid Detection of AD Requests for Smartphone Platforms." In 2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC). IEEE, 2023. http://dx.doi.org/10.1109/icaaic56838.2023.10140512.

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Zhu, Tong, Chaofan Shou, Zhen Huang, et al. "Unveiling Collusion-Based Ad Attribution Laundering Fraud: Detection, Analysis, and Security Implications." In CCS '24: ACM SIGSAC Conference on Computer and Communications Security. ACM, 2024. https://doi.org/10.1145/3658644.3670314.

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Sun, Suibin, Le Yu, Xiaokuan Zhang, et al. "Understanding and Detecting Mobile Ad Fraud Through the Lens of Invalid Traffic." In CCS '21: 2021 ACM SIGSAC Conference on Computer and Communications Security. ACM, 2021. http://dx.doi.org/10.1145/3460120.3484547.

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