Click Fraud Prevention via multimodal evidence fusion by Dempster-Shafer theory

Kantardzic, Mehmed; Walgampaya, Chamila; Yampolskiy, Roman V.; Woo, Ryu Joung

doi:10.1109/mfi.2010.5604480

Cited by 11 publications

(6 citation statements)

References 11 publications

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“…In [83], Kantardzic et al have introduced a multi-modal real-time detection and prevention system to address the problem of click fraud in the online ad system. The proposed model is based on Collaborative Click Fraud Detection and Prevention (CCFDP) system [84] involving a collaboration between client-side logs and server-side logs.…”

Section: Traffic Fraud Detectionmentioning

confidence: 99%

Ads and Fraud: A Comprehensive Survey of Fraud in Online Advertising

Sadeghpour

Vlajic

2021

JCP

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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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Section: Traffic Fraud Detectionmentioning

confidence: 99%

Ads and Fraud: A Comprehensive Survey of Fraud in Online Advertising

Sadeghpour

Vlajic

2021

JCP

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“…It was first detected and reported by the NetMosaics click fraud detection system in 2010 [10], [9], [11]. Smart ClickBot is a for-sale click bot and it can be purchased [20].…”

Section: An Overview Of the Smart Clickbotmentioning

confidence: 99%

“…What is left in the server side is separately analyzed, which is the scope of this paper, for potential bot networks. We have discussed the functionality of our system in detail in [10], [9], and [11].…”

Section: A Data Collection Pre-processing and Session Identificationmentioning

confidence: 99%

Cracking the Smart ClickBot

Walgampaya

Kantardzic

2011

2011 13th IEEE International Symposium on Web Systems Evolution (WSE)

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Nowadays, almost every task involving Web traversing and information retrieval recurs to Web robots. Web robots are software programs that automatically traverse the Web's hypertext structure. They proliferate rapidly aside with the growth of the Web and are extremely valuable and important means not only for the large search engines, but also for many specialized services such as investment portals, competitive intelligence tools, etc. While many web robots serve useful purposes, recently, there have been cases linked to fraudulent activities committed by these Web robots. Click fraud, which is the act of generating illegitimate clicks, is one of them. This paper details the architecture and functionality of the Smart ClickBot, a sophisticated software bot that is designed to commit click fraud. It was first detected and reported by NetMosaics Inc. in March, 2010, a real time click fraud detection and prevention solution provider. We discuss the machine learning algorithms used, to identify all clicks exhibiting Smart ClickBot like patterns. We constructed a Bayesian classifier that automatically classifies server log data as being Smart ClickBot or not. We also introduce a Benchmark data set for Smart ClickBot. We disclose the results of our investigation of this bot to educate the security research community and provide information regarding the novelties of the attack.

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“…Implementation details of the system are discussed in [Kantardzic et al, 2010b], and [Walgampaya et al, 2010]. This work is primarily motivated by the tradeoff between space usage and accuracy of the existing duplicate detection systems.…”

Section: Bloom Filters For Duplicate Detectionmentioning

confidence: 99%

“…For example in a given web server average number of clicks per unit time is usually known , which we refer to as baselines in our previous papers [Walgampaya et al, 2010, Kantardzic et al, 2010b. Therefore the only parameter we can vary is the number of hash functions.…”

Section: Accuracy Of the Bloom Filtermentioning

confidence: 99%

Click fraud : how to spot it, how to stop it?

Walgampaya

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Click Fraud Prevention via multimodal evidence fusion by Dempster-Shafer theory

Cited by 11 publications

References 11 publications

Ads and Fraud: A Comprehensive Survey of Fraud in Online Advertising

Ads and Fraud: A Comprehensive Survey of Fraud in Online Advertising

Cracking the Smart ClickBot

Click fraud : how to spot it, how to stop it?

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