Chamila Walgampaya

Improving Click Fraud Detection by Real Time Data Fusion

Click fraud is a type of Internet crime that occurs in pay per click online advertising when a person, automated script, or computer program imitates a legitimate user of a Web browser clicking on an ad, for the purpose of generating a charge per click without having actual interest in the target of the ads link. Most of the available commercial solutions are just click fraud reporting systems, not real-time click fraud detection and prevention systems. A new solution is proposed in this paper that will analyze the detailed user click activities based on data collected form different sources. More information about each click enables better evaluation of the quality of click traffic. We utilize the multi source data fusion to merge client side and server side activities. Proposed solution is integrated in our CCFDP V1.0 system for a real-time detection and prevention of click fraud. We have tested the system with real world data from an actual ad campaign where the results show that additional real-time information about clicks improve the quality of click fraud analysis.

Cracking the Smart ClickBot

Nowadays, almost every task involving Web traversing and information retrieval recurs to Web robots. Web robots are software programs that automatically traverse the Webs 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.

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

We address the problem of combining information from diversified sources in a coherent fashion. A generalized evidence processing theory and an architecture for data fusion that accommodates diversified sources of information are presented. Different levels at which data fusion may take place such as the level of dynamics, the level of attributes, and the level of evidence are discussed. A multi-level fusion architecture based Collaborative Click Fraud Detection and Prevention (CCFDP) system for real time click fraud detection and prevention is proposed and its performance is compared with a traditional rule based click fraud detection system. Both systems are tested with real world data from an actual ad campaign. Results show that use of multi-level data fusion improves the quality of click fraud analysis.

Click fraud prevention in pay-per-click model: Learning through multi-model evidence fusion

Multi-sensor data fusion has been an area of intense recent research and development activity. This concept has been applied to numerous fields and new applications are being explored constantly. Multi-sensor based Collaborative Click Fraud Detection and Prevention (CCFDP) system can be viewed as a problem of evidence fusion. In this paper we detail the multi level data fusion mechanism used in CCFDP for real time click fraud detection and prevention. Prevention mechanisms are based on blocking suspicious traffic by IP, referrer, city, country, ISP, etc. Our system maintains an online database of these suspicious parameters. We have tested the system with real-world data from an actual ad campaign where the results show that use of multilevel data fusion improves the quality of click fraud analysis.

Selection of Distributed Sensors for Multiple Time Series Prediction

In this paper we are proposing a methodology for the selection of a subset of sensors for a prediction system of a total energy production in a region. The network of wireless sensors is distributed close to power plants and generates multiple time series data representing energy production of each plant. Our study shows that we can estimate the total energy using significantly reduced number of sensors. To build the prediction model we have used three common forecasting techniques, support vector machines (SVMs), Multilayer Perceptron (MLP), and Multiple Regression (MR). For training and testing of the models we have used the data from year 2002 to 2004. Our data set consists of 201 attributes. First 200 represents the data from sensor stations and the additional variable is the total energy production. Based on our results MR technique gives the best model for the prediction and it outperforms the MLP and SVM. We analyzed the quality of prediction with different subsets of sensors. Based on this study we have estimated the minimum number of sensors required for the prediction model. We also estimated the optimum number of sensors that will balance the expenses of the system with the accuracy. Proposed model and performed analysis may embody crucial information for producers and consumers when planning bidding strategies in energy trading in order to maximize their benefits and utilities.

Evidence Fusion for Real Time Click Fraud Detection and Prevention

From the viewpoint of Dempster-Shafer evidence theory, information obtained from different sources can be considered as pieces of evidence, and as such, multi-sensor based CCFDP (Collaborative Click Fraud Detection and Prevention) system can be viewed as a problem of evidence fusion. In this paper we detail the multi level data fusion mechanism used in CCFDP for real time click fraud detection and prevention. Prevention mechanisms are based on blocking suspicious traffic by IP, referrer, city, country, ISP, etc. Our system maintains an online database of these suspicious parameters. We have tested the system with real world data from an actual ad campaign where the results show that use of multi-level data fusion improves the quality of click fraud analysis.

Duplicate detection in pay-per-click streams using temporal stateful Bloom filters

Detecting duplicates in click data streams is an important task to fight against click fraud, which is the act of generating false clicks in internet advertising. Revenue generation advertising models, that charge advertisers for each click, leave space for individuals or rival companies to generate false clicks. The extent of click frauds damage to online advertising has grown tremendously over the years. In this paper, we consider the problem of detecting duplicates in click data streams. Our solution uses a modified version of the counting Bloom filter. The temporal stateful Bloom filter (TSBF) extends the standard counting Bloom filter by replacing the bit-vector with an array of counters of states. These counters are dynamic and decay with time. We conducted a comprehensive set of experiments using synthetic and real world data. Results are compared with buffering techniques used in NetMosaics, a click fraud detection and prevention solution. Our results show that TSBF approach achieves 99% accuracy on duplicate detection, while keeping its space requirement a constant.