## 9 Conclusions and Future Trends  
Online advertising fraud aims to exfiltrate money from the ad ecosystem by conducting various types of fraudulent actions. This survey has provided a comprehensive study of online digital advertising platforms, their key components and the most popular revenue models. Additionally, various techniques to collect data on users' online activities and their behavioral patterns have also been reviewed. More importantly, the present work adds to the existing literature in this field by focusing on the categorization of ad fraud based on the main human actors--executors vs. victims--and providing illustrative real-world examples of different types of advertising fraud; this essential practical perspective makes it different from previous studies such as [4, 5]. Most existing works have discussed advertising fraud in very generic ways. They focused on issues relating to privacy threats and protection mechanisms [101, 5], marketing perspective [102, 103, 104] and analytical assessment on challenges in online advertising [105, 106]. Through this study, the following observations have been made:  
There has been a major shift in the academic circles towards the use of machine learning (ML) and artificial intelligence (AI) solutions--some of these works are surveyed in this paper. The shift towards ML/AI solutions is observed in the industry as well. This shift stems from the well-established capabilities of ML/AI to effectively analyze users' needs, interests, and preferences--in real-time and from large volumes of multisensory data--ultimately producing comprehensive consumer insights. In particular, as described in [107, 108], ML/AI based speech recognition, natural language processing, and the help of computer vision technologies are very useful for understanding consumer insights into online advertising. Additionally, the studies presented in [109, 110, 111, 112] describe the broader use of intelligent advertising in practice for advertising creativity with diverse consumers and as sponsored recommendations on retail platforms. As real-world examples of ML/AI deployment, Facebook, Google, and Amazon--the biggest names in the digital advertising industry--have begun investing in ML/AI solutions in their online advertising platforms [113, 114]. The Google Assistant, introduced in 2016, can pull data from any Google app (e.g., Gmail, Search, Maps, Shopping, Photos, Calendar, Contacts,... ) after only a single sign-in by the user. It then uses AI technologies, such as natural language processing and machine learning, to perform various tasks responding to voice requests or user keyboard input [115]. However, [PERSON] in [115] has claimed that although intelligent advertising seems to be driven by big data, algorithms, and cloud computing, its ultimate future may depend on other factors, such as industry innovation, investment in AI technologies, government regulation, and business and consumer acceptance [116, 117]. On the other hand, in the report by the Juniper research group [118], it is stated that the publishers' ad media inventory grows faster than advertisers' demand. This gap provides an exclusive opportunity for fraudsters to fill this unused inventory with fraudulent ads through advanced techniques such as spoofed advertising networks and fabricated ad clicks. To address the problem of sophisticated ad fraud, we believe marketers should embrace a holistic approach to data analysis. This involves connecting the data dots across networks, browsers, and devices as well as user behavior to complete view of the entire ad ecosystem. Using big data to distinguish between genuine and fraudulent ad requests, the new generation of machine learning techniques (i.e., Deep Reinforcement Learning [119])--which combines artificial neural networks with a reinforcement learning architecture--can be deployed. Reinforcement learning or goal-oriented algorithms learn how to achieve a complicated goal or maximize an objective along a given dimension over many steps.