Crowdsourcing for click fraud detection by Riwa Mouawi, Imad H. Elhajj, Ali Chehab, Ayman Kayssi

Subjects: “…Click fraud…”
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Click Fraud in Digital Advertising: A Comprehensive Survey by Shadi Sadeghpour, Natalija Vlajic

“…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. …”
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CAT-RFE: ensemble detection framework for click fraud by Yixiang LU, Guanggang GENG, Zhiwei YAN, Xiaomin ZHU, Xinchang ZHANG

Subjects: “…click fraud detection…”
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A hybrid and effective learning approach for Click Fraud detection by Thejas G.S., Surya Dheeshjith, S.S. Iyengar, N.R. Sunitha, Prajwal Badrinath

Subjects: “…Click Fraud…”
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Click fraud detection for online advertising using machine learning by Malak Aljabri, Rami Mustafa A. Mohammad

Subjects: “…Click fraud…”
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AI-Based Techniques for Ad Click Fraud Detection and Prevention: Review and Research Directions by Reem A. Alzahrani, Malak Aljabri

Subjects: “…click fraud…”
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An Ensemble Architecture Based on Deep Learning Model for Click Fraud Detection in Pay-Per-Click Advertisement Campaign by Amreen Batool, Yung-Cheol Byun

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Quad division prototype selection-based k-nearest neighbor classifier for click fraud detection from highly skewed user click dataset by Deepti Sisodia, Dilip Singh Sisodia

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Not-a-Bot (NAB): Improving Service Availability in the Face of Botnet Attacks by Gummadi, Ramakrishna, Balakrishnan, Hari, Maniatis, Petros, Ratnasamy, Sylvia

“…A large fraction of email spam, distributed denial-of-service (DDoS) attacks, and click-fraud on web advertisements are caused by traffic sent from compromised machines that form botnets. …”
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Smart Approach for Botnet Detection Based on Network Traffic Analysis by Alaa Obeidat, Rola Yaqbeh

“…Cybercriminals have exploited botnets for many illegal activities, including click fraud, DDOS attacks, and spam production. In this article, we suggest a method for identifying the behavior of data traffic using machine learning classifiers including genetic algorithm to detect botnet activities. …”
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AdSelector: A privacy-preserving advertisement selection mechanism for mobile devices by Liu, Y, Simpson, A

“…In particular: (1) the user subscription mechanism helps users to identify their interests and subscribe to desirable categories of ads; (2) the two-stage ad selection process ensures that ad servers can only obtain coarse-grained user profiles, with fine-grained user profiles stored and used only on the mobile devices; and (3) the trustworthy billing system helps to report ad-clicks without revealing users' identities and assists in detecting click-fraud attacks. The performance of the mechanism is evaluated in the context of a prototype privacy-preserving targeted mobile advertising framework.…”

Blockchain Implications for Marketing; A Review and an Empirical Analysis by Taher M. Al-Ahwal, Dušan Mladenović, Ahad ZareRavasan

“…This research was set out to investigate and evaluate six benefits of blockchain for marketing: fostering disintermediation, combating click fraud, reinforcing trust and transparency, enhancing privacy protection, empowering digital marketing security, and enabling creative loyalty programs. …”
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A Review Paper on Botnet and Botnet Detection Techniques in Cloud Computing by Shahid, Anwar, Jasni, Mohamad Zain, Mohamad Fadli, Zolkipli, Zakira, Inayat

“…The attackers use these botnets for criminal activities such as DDoS, click fraud, phishing, spamming, sniffing traffic and spreading new malware. …”
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SMARTbot: A Behavioral Analysis Framework Augmented with Machine Learning to Identify Mobile Botnet Applications. by Ahmad Karim, Rosli Salleh, Muhammad Khurram Khan

“…It refers to the network of computers, laptops, mobile devices or tablets which is remotely controlled by the cybercriminals to initiate various distributed coordinated attacks including spam emails, ad-click fraud, Bitcoin mining, Distributed Denial of Service (DDoS), disseminating other malwares and much more. …”
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How Blockchain Technology Can Benefit Marketing: Six Pending Research Areas by Abderahman Rejeb, John G. Keogh, Horst Treiblmaier

“…Moreover, blockchain technology fosters disintermediation, aids in combatting click fraud, reinforces trust and transparency, enables enhanced privacy protection, empowers security, and enables creative loyalty programs. …”
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Multilayer framework for botnet detection using machine learning algorithms by Ibrahim, W. N. H., Anuar, S., Selamat, A., Krejcar, O., Crespo, R. G., Viedma, E. H., Fujita, H.

“…Botnet can perform massive cyber-attacks such as DDOS, SPAM, click-fraud, information, and identity stealing. The botnet also can avoid being detected by a security system. …”
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Unfair Competition Issues of Big Data in China by Huang-Chih Sung

“…Since 2015, more and more unfair competition cases concerning big data have occurred in China, such as masking advertisement, click fraud, malicious incompatibility, and gathering user’s personal data from competitors by unfair means, which can be categorized to unfair competition about illegal collection/use of competitors’ big data and about network traffic. …”
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AGCN-Domain: Detecting Malicious Domains with Graph Convolutional Network and Attention Mechanism by Xi Luo, Yixin Li, Hongyuan Cheng, Lihua Yin

“…Considering the foundation and openness of DNS, it is not surprising that adversaries register massive domains to enable multiple malicious activities, such as spam, command and control (C&C), malware distribution, click fraud, etc. Therefore, detecting malicious domains is a significant topic in security research. …”
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A Meta-Classification Model for Optimized ZBot Malware Prediction Using Learning Algorithms by Shanmugam Jagan, Ashish Ashish, Miroslav Mahdal, Kenneth Ruth Isabels, Jyoti Dhanke, Parita Jain, Muniyandy Elangovan

“…Botnets pose a real threat to cybersecurity by facilitating criminal activities like malware distribution, attacks involving distributed denial of service, fraud, click fraud, phishing, and theft identification. The methods currently used for botnet detection are only appropriate for specific botnet commands and control protocols; they do not endorse botnet identification in early phases. …”
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Multilayer Framework for Botnet Detection Using Machine Learning Algorithms by Wan Nur Hidayah Ibrahim, Syahid Anuar, Ali Selamat, Ondrej Krejcar, Ruben Gonzalez Crespo, Enrique Herrera-Viedma, Hamido Fujita

“…Botnet can perform massive cyber-attacks such as DDOS, SPAM, click-fraud, information, and identity stealing. The botnet also can avoid being detected by a security system. …”
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