The Experts below are selected from a list of 12045 Experts worldwide ranked by ideXlab platform
David J Hand - One of the best experts on this subject based on the ideXlab platform.
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statistical Fraud Detection a review
Statistical Science, 2002Co-Authors: Richard J. Bolton, David J HandAbstract:Fraud is increasing dramatically with the expansion of modem technology and the global superhighways of communication, resulting in the loss of billions of dollars worldwide each year. Although prevention technologies are the best way to reduce Fraud, Fraudsters are adaptive and, given time, will usually find ways to circumvent such measures. Methodologies for the Detection of Fraud are essential if we are to catch Fraudsters once Fraud prevention has failed. Statistics and machine learning provide effective technologies for Fraud Detection and have been applied successfully to detect activities such as money laundering, e-commerce credit card Fraud, telecommunications Fraud and computer intrusion, to name but a few. We describe the tools available for statistical Fraud Detection and the areas in which Fraud Detection technologies are most used.
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Fraud Detection : Statistical
Statistical Science, 2002Co-Authors: J Bolton, David J HandAbstract:Fraud is increasing dramatically with the expansion of modem technology and the global superhighways of communication, resulting in the loss of billions of dollars worldwide each year. Although prevention technologies are the best way to reduce Fraud, Fraudsters are adaptive and, given time, will usually find ways to circumvent such measures. Methodologies for the Detection of Fraud are essential if we are to catch Fraudsters once Fraud prevention has failed. Statistics and machine learning provide effective technologies for Fraud Detection and have been applied successfully to detect activities such as money laundering, e-commerce credit card Fraud, telecommunications Fraud and computer intrusion, to name but a few. We describe the tools available for statistical Fraud Detection and the areas in which Fraud Detection technologies are most used. Key
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Fraud Detection: A Review
Statistical Science, 2002Co-Authors: Richard J. Bolton, David J HandAbstract:Fraud is increasing dramatically with the expansion of modem technology and the global superhighways of communication, resulting in the loss of billions of dollars worldwide each year. Although prevention technologies are the best way to reduce Fraud, Fraudsters are adaptive and, given time, will usually find ways to circumvent such measures. Methodologies for the Detection of Fraud are essential if we are to catch Fraudsters once Fraud prevention has failed. Statistics and machine learning provide effective technologies for Fraud Detection and have been applied successfully to detect activities such as money laundering, e-commerce credit card Fraud, telecommunications Fraud and computer intrusion, to name but a few. We describe the tools available for statistical Fraud Detection and the areas in which Fraud Detection technologies are most used
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Statistical Fraud Detection: A ReviewCommentCommentRejoinder
Statistical Science, 2002Co-Authors: Richard J. Bolton, David J Hand, Foster Provost, Leo BreimanAbstract:Fraud is increasing dramatically with the expansion of modern technology and the global superhighways of communication, resulting in the loss of billions of dollars worldwide each year. Although prevention technologies are the best way to reduce Fraud, Fraudsters are adaptive and, given time, will usually find ways to circumvent such measures. Methodologies for the Detection of Fraud are essential if we are to catch Fraudsters once Fraud prevention has failed. Statistics and machine learning provide effective technologies for Fraud Detection and have been applied successfully to detect activities such as money laundering, e-commerce credit card Fraud, telecommunications Fraud and computer intrusion, to name but a few. We describe the tools available for statistical Fraud Detection and the areas in which Fraud Detection technologies are most used.
Richard J. Bolton - One of the best experts on this subject based on the ideXlab platform.
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statistical Fraud Detection a review
Statistical Science, 2002Co-Authors: Richard J. Bolton, David J HandAbstract:Fraud is increasing dramatically with the expansion of modem technology and the global superhighways of communication, resulting in the loss of billions of dollars worldwide each year. Although prevention technologies are the best way to reduce Fraud, Fraudsters are adaptive and, given time, will usually find ways to circumvent such measures. Methodologies for the Detection of Fraud are essential if we are to catch Fraudsters once Fraud prevention has failed. Statistics and machine learning provide effective technologies for Fraud Detection and have been applied successfully to detect activities such as money laundering, e-commerce credit card Fraud, telecommunications Fraud and computer intrusion, to name but a few. We describe the tools available for statistical Fraud Detection and the areas in which Fraud Detection technologies are most used.
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Fraud Detection: A Review
Statistical Science, 2002Co-Authors: Richard J. Bolton, David J HandAbstract:Fraud is increasing dramatically with the expansion of modem technology and the global superhighways of communication, resulting in the loss of billions of dollars worldwide each year. Although prevention technologies are the best way to reduce Fraud, Fraudsters are adaptive and, given time, will usually find ways to circumvent such measures. Methodologies for the Detection of Fraud are essential if we are to catch Fraudsters once Fraud prevention has failed. Statistics and machine learning provide effective technologies for Fraud Detection and have been applied successfully to detect activities such as money laundering, e-commerce credit card Fraud, telecommunications Fraud and computer intrusion, to name but a few. We describe the tools available for statistical Fraud Detection and the areas in which Fraud Detection technologies are most used
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Statistical Fraud Detection: A ReviewCommentCommentRejoinder
Statistical Science, 2002Co-Authors: Richard J. Bolton, David J Hand, Foster Provost, Leo BreimanAbstract:Fraud is increasing dramatically with the expansion of modern technology and the global superhighways of communication, resulting in the loss of billions of dollars worldwide each year. Although prevention technologies are the best way to reduce Fraud, Fraudsters are adaptive and, given time, will usually find ways to circumvent such measures. Methodologies for the Detection of Fraud are essential if we are to catch Fraudsters once Fraud prevention has failed. Statistics and machine learning provide effective technologies for Fraud Detection and have been applied successfully to detect activities such as money laundering, e-commerce credit card Fraud, telecommunications Fraud and computer intrusion, to name but a few. We describe the tools available for statistical Fraud Detection and the areas in which Fraud Detection technologies are most used.
Foster Provost - One of the best experts on this subject based on the ideXlab platform.
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Statistical Fraud Detection: A ReviewCommentCommentRejoinder
Statistical Science, 2002Co-Authors: Richard J. Bolton, David J Hand, Foster Provost, Leo BreimanAbstract:Fraud is increasing dramatically with the expansion of modern technology and the global superhighways of communication, resulting in the loss of billions of dollars worldwide each year. Although prevention technologies are the best way to reduce Fraud, Fraudsters are adaptive and, given time, will usually find ways to circumvent such measures. Methodologies for the Detection of Fraud are essential if we are to catch Fraudsters once Fraud prevention has failed. Statistics and machine learning provide effective technologies for Fraud Detection and have been applied successfully to detect activities such as money laundering, e-commerce credit card Fraud, telecommunications Fraud and computer intrusion, to name but a few. We describe the tools available for statistical Fraud Detection and the areas in which Fraud Detection technologies are most used.
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Fraud Detection
Handbook of data mining and knowledge discovery, 2002Co-Authors: Tom Fawcett, Foster ProvostAbstract:Fraud is the deliberate use of deception to conduct illicit activities. Automatic Fraud Detection involves scanning large volumes of data to uncover patterns of fradulent usage, and as such it is well suited to data mining techniques. We present three general types of Fraud that have been addressed in data mining research, and we summarize the approaches taken. We also discuss general characteristics of Fraud Detection problems that make them difficult, as well as system integration issues for automatic Fraud Detection systems.
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AI Approaches to Fraud Detection and Risk Management
Ai Magazine, 1998Co-Authors: Tom Fawcett, Foster Provost, Ira J. Haimowitz, Salvatore J. StolfoAbstract:The 1997 AAAI Workshop on AI Approaches to Fraud Detection and Risk Management brought together over 50 researchers and practitioners to discuss problems of Fraud Detection, computer intrusion Detection, and risk scoring. This article presents highlights, including discussions of problematic issues that are common to these application domains, and proposed solutions that apply a variety of AI techniques.
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Adaptive Fraud Detection
Data Mining and Knowledge Discovery, 1997Co-Authors: Tom Fawcett, Foster ProvostAbstract:One method for detecting Fraud is to check for suspicious changes in user behavior. This paper describes the automatic design of user profiling methods for the purpose of Fraud Detection, using a series of data mining techniques. Specifically,we use a rule-learning program to uncover indicators of Fraudulent behavior from a large database of customer transactions. Then the indicators are used to create a set of monitors, which profile legitimate customer behavior and indicate anomalies. Finally, the outputs of the monitors are used as features in a system that learns to combine evidence to generate high-confidence alarms. The system has been applied to the problem of detecting cellular cloning Fraud based on a database of call records. Experiments indicate that this automatic approach performs better than hand-crafted methods for detecting Fraud. Furthermore, this approach can adapt to the changing conditions typical of Fraud Detection environments.
E.l. Barse - One of the best experts on this subject based on the ideXlab platform.
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Logging for Intrusion and Fraud Detection
2017Co-Authors: E.l. BarseAbstract:Computer security is an area of ever increasing importance. Our society relies on computerised services, which gives many reasons for computer criminals, attackers, terrorists, hackers, crackers, Fraudsters, or whatever name is appropriate, to break these systems. To deal with security problems, many types of mechanisms have been developed. One mechanism is the intrusion Detection system (IDS), designed to detect ongoing attacks, detect attacks after the fact or even detect preparations for an attack. The IDS is complementary to preventive security mechanisms, such as firewalls and authentication systems, which can never be made 100% secure. A similar type of system is the Fraud Detection system (FDS), specialised to detect Frauds (or "attacks") in commercial services in different business areas, such as telecom, insurance and banking. Fraud Detection can be considered a special case of intrusion Detection. A crucial part of intrusion or Fraud Detection is to have good quality input data for the analysis, as well as for training and testing the systems. However, it is difficult to acquire any training and test data and it is not known what kind of log data are most suitable to use for Detection. The contribution of this thesis is to offer guidance in matters of acquiring more suitable log data for intrusion and Fraud Detection. The first part is general and gives a survey of research done in intrusion Detection and shows that intrusion and Fraud Detection reflect different aspects of the same problem. The second part is devoted to improving the availability and quality of log data used in intrusion and Fraud Detection. The availability of log data for training and testing Detection systems can be improved by solving the privacy issues that prevent computer system owners from releasing their log data. Therefore, a method is suggested for anonymising the log data in a way that does not significantly affect their usefulness for Detection. Though authentic data are convenient to use for training and testing they do not always have the desirable properties, which include flexibility and control of content. Another contribution to improve the availability and also the quality of log data is thus a method for creating synthetic training and test data with suitable properties. This part also includes a methodology for determining exactly which log data can be used for detecting specific attacks. In the ideal situation, we only collect exactly the data needed for Detection, and this methodology can help us develop more efficient and adapted log sources. These new log sources will improve the quality of log data used for intrusion and Fraud Detection.
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Logging for intrusion and Fraud Detection
Doktorsavhandlingar vid Chalmers Tekniska Hogskola, 2004Co-Authors: E.l. BarseAbstract:Various issues which deal with security problems of logging and data collection for intrusion and Fraud Detection, are discussed. One mechanism is the intrusion Detection system (IDS), designed to detect ongoing attacks, detect attacks after the fact or even detect preparations for an attack. A similar type of system is the Fraud Detection system (FDS), which is specialized to detect Frauds in commercial services in different business areas such as telecom, insurance and banking. High quality log data can improve coverage of attacks, lower the false alarm rate, improve efficiency of Detection systems, and facilitate better testing and evaluation procedures.
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ACSAC - Synthesizing test data for Fraud Detection systems
19th Annual Computer Security Applications Conference 2003. Proceedings., 2003Co-Authors: E.l. Barse, Hanna Kvarnström, Erland JonssonAbstract:We report an experiment aimed at generating synthetic test data for Fraud Detection in an IP based video-on-demand service. The data generation verifies a methodology previously developed by the present authors [E. Lundin et al., (2002)] that ensures that important statistical properties of the authentic data are preserved by using authentic normal data and Fraud as a seed for generating synthetic data. This enables us to create realistic behavior profiles for users and attackers. The data is used to train the Fraud Detection system itself, thus creating the necessary adaptation of the system to a specific environment. Here we aim to verify the usability and applicability of the synthetic data, by using them to train a Fraud Detection system. The system is then exposed to a set of authentic data to measure parameters such as Detection capability and false alarm rate as well as to a corresponding set of synthetic data, and the results are compared.
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Synthesizing test data for Fraud Detection systems
19th Annual Computer Security Applications Conference 2003. Proceedings., 2003Co-Authors: E.l. Barse, Hanna Kvarnström, E. JohnsonAbstract:We report an experiment aimed at generating synthetic test data for Fraud Detection in an IP based video-on-demand service. The data generation verifies a methodology previously developed by the present authors [E. Lundin et al., (2002)] that ensures that important statistical properties of the authentic data are preserved by using authentic normal data and Fraud as a seed for generating synthetic data. This enables us to create realistic behavior profiles for users and attackers. The data is used to train the Fraud Detection system itself, thus creating the necessary adaptation of the system to a specific environment. Here we aim to verify the usability and applicability of the synthetic data, by using them to train a Fraud Detection system. The system is then exposed to a set of authentic data to measure parameters such as Detection capability and false alarm rate as well as to a corresponding set of synthetic data, and the results are compared.
Leo Breiman - One of the best experts on this subject based on the ideXlab platform.
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Statistical Fraud Detection: A ReviewCommentCommentRejoinder
Statistical Science, 2002Co-Authors: Richard J. Bolton, David J Hand, Foster Provost, Leo BreimanAbstract:Fraud is increasing dramatically with the expansion of modern technology and the global superhighways of communication, resulting in the loss of billions of dollars worldwide each year. Although prevention technologies are the best way to reduce Fraud, Fraudsters are adaptive and, given time, will usually find ways to circumvent such measures. Methodologies for the Detection of Fraud are essential if we are to catch Fraudsters once Fraud prevention has failed. Statistics and machine learning provide effective technologies for Fraud Detection and have been applied successfully to detect activities such as money laundering, e-commerce credit card Fraud, telecommunications Fraud and computer intrusion, to name but a few. We describe the tools available for statistical Fraud Detection and the areas in which Fraud Detection technologies are most used.
