scholarly journals Click Fraud in Digital Advertising: A Comprehensive Survey

Computers ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 164
Author(s):  
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. 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.

2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S534-S535
Author(s):  
Charlotte-Paige M Rolle ◽  
Vu Nguyen ◽  
Kiran Patel ◽  
Dan Cruz ◽  
Federico Hinestrosa ◽  
...  

Abstract Background Approximately 50% of people living with HIV (PLWH) in the United States are ≥50 years old. Efforts are ongoing to identify antiretrovirals associated with fewer drug-drug interactions (DDIs) and long-term side effects in this group. Clinical trials of B/F/TAF demonstrated favorable efficacy and safety in older adults, however, data from real-word settings are needed to validate these results. Methods This retrospective analysis evaluated records from PLWH aged ≥ 50 years at the Orlando Immunology Center who were switched to B/F/TAF between 2/7/2018 and 5/31/2019. Eligible patients had baseline HIV-1 RNA< 50 copies/mL and were followed for 48 weeks post-switch. The primary endpoint was maintenance of HIV-1 RNA< 50 copies/mL at week 48. The impact of switching to B/F/TAF on DDIs, adverse events (AEs) and safety parameters were analyzed throughout the study. Results 306 patients met inclusion criteria. 62 (20%) were female, 126 (41%) were non-white, median age was 58 years (range [r] 50-81), median duration of HIV infection was 19.5 years (r 2-40), median number of chronic co-morbid conditions was 5 (r 0-20), and median number of baseline concomitant medications was 4 (r 0-23). 159 (52%) patients were switched from regimens containing ritonavir or cobicistat. The most commonly documented reason for switch was simplification (Table 1). At Week 48, 287 (94%) patients maintained an HIV-1 RNA< 50 copies/ml and 19 (6%) had an HIV-1 RNA between 50-200 copies/mL (Figure 1). 1 patient discontinued due to lack of efficacy. A total of 123 potential DDIs were identified in 104 (34%) patients taking a boosting agent or rilpivirine at baseline (Table 2). At Week 48, there was a significant median decline in total cholesterol (15.5 mg/dL, 95% confidence interval [CI]: 9.5; 21.5), LDL cholesterol (9.5 mg/dL, 95% CI: 4; 15.5) and triglycerides (20 mg/dL, 95% CI: 9.5; 32.5), and median weight increased by 2.5 pounds (95% CI: 1.5; 3.5). Treatment-related AEs occurred in 33 (11%) patients (all Grade 1-2) and led to 7 (2%) discontinuations. Table 1-Baseline demographic and clinical characteristics Table 2-Avoidance of Drug-Drug Interactions (DDIs) following switch to B/F/TAF Figure 1-Subgroup analysis of virologic outcomes at Week 48 Conclusion In this real-world cohort, switching to B/F/TAF was associated with maintenance of virologic control, improvement in lipid parameters, and avoidance of DDIs in a large proportion of patients. These data support use of B/F/TAF as a treatment option in older PLWH. Disclosures Charlotte-Paige M. Rolle, MD MPH, Gilead Sciences (Grant/Research Support, Scientific Research Study Investigator, Speaker’s Bureau)Janssen Infectious Disease (Grant/Research Support)ViiV Healthcare (Grant/Research Support, Scientific Research Study Investigator, Advisor or Review Panel member, Speaker’s Bureau) Kiran Patel, PharmD, Gilead Sciences (Employee) Federico Hinestrosa, MD, AbbVie (Speaker’s Bureau)Gilead Sciences (Speaker’s Bureau)Merck (Speaker’s Bureau)Theratechnologies (Speaker’s Bureau) Edwin DeJesus, MD, Gilead Sciences (Advisor or Review Panel member)


2021 ◽  
Vol 4 (4) ◽  
pp. 10-22
Author(s):  
Patricia R. DeLucia ◽  
Amanda L. Woods ◽  
Jeong-Hee Kim ◽  
Ngan Nguyen ◽  
Eugene W. Wang ◽  
...  

This research study at a National Science Foundation Research Experience for Undergraduates site focuses on psychological research with applications to the real world. Two cohorts of undergraduates engaged in rigorous research projects on, e.g., driving, homeland security, relationships, human-computer interaction, language comprehension and production, discrimination, and health psychology. Results indicated that students and mentors perceived an improvement in the students' research skills.


2018 ◽  
Vol 45 (2) ◽  
pp. 156-168 ◽  
Author(s):  
Mahsa Seifikar ◽  
Saeed Farzi

Recently, social networks have provided an important platform to detect trends of real-world events. The trends of real-world events are detected by analysing flow of massive bulks of data in continuous time steps over various social media platforms. Today, many researchers have been interested in detecting social network trends, in order to analyse the gathered information for enabling users and organisations to satisfy their information need. This article is aimed at complete surveying the recent text-based trend detection approaches, which have been studied from three perspectives (algorithms, dimension and diversity of events). The advantages and disadvantages of the considered approaches have also been paraphrased separately to illustrate a comprehensive view of the previous works and open problems.


2021 ◽  
Author(s):  
Jesper Christensen

This is a comprehensive study exploring a number of innovativeapproaches to efficient crash structure design for automotive applications.The study is completed using a novel reduced order modelling approachenabling a detailed investigation that is not computationally prohibitive. The study includes a number of innovative designs with significant potential for dramatically increasing specific energy absorbance, but also highlights that some of these are more prone to a number of problematic aspects relating to real world implementation.


2019 ◽  
Vol 33 (15) ◽  
pp. 1950150 ◽  
Author(s):  
Lijiao Pan ◽  
Shibiao Mu ◽  
Yingyan Wang

A user click fraud detection method based on Top-Rank-k frequent pattern mining algorithm is presented to solve the click fraud problem appearing in current online advertising. Firstly, this method combines the click frequency of event samples, calculates the real evaluation score of click stream, and the click stream density function and evaluation score expression under multi-dimensional variables, and further obtains the time complexity of the next user’s click fraud process. Secondly, according to the Top-Rank-k frequent pattern, the process of click fraud detection algorithm is designed, and the click fraud user is analyzed and obtained. The results show that this method has good efficiency and correctness, and is superior to other similar algorithms.


2016 ◽  
Vol 30 (4) ◽  
pp. 867-882 ◽  
Author(s):  
Ayesha I.T. Tulloch ◽  
Alessio Mortelliti ◽  
Geoffrey M. Kay ◽  
Daniel Florance ◽  
David Lindenmayer

Author(s):  
Wen Xu ◽  
Jing He ◽  
Yanfeng Shu

Transfer learning is an emerging technique in machine learning, by which we can solve a new task with the knowledge obtained from an old task in order to address the lack of labeled data. In particular deep domain adaptation (a branch of transfer learning) gets the most attention in recently published articles. The intuition behind this is that deep neural networks usually have a large capacity to learn representation from one dataset and part of the information can be further used for a new task. In this research, we firstly present the complete scenarios of transfer learning according to the domains and tasks. Secondly, we conduct a comprehensive survey related to deep domain adaptation and categorize the recent advances into three types based on implementing approaches: fine-tuning networks, adversarial domain adaptation, and sample-reconstruction approaches. Thirdly, we discuss the details of these methods and introduce some typical real-world applications. Finally, we conclude our work and explore some potential issues to be further addressed.


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