Intrusion detection systems and multisensor data fusion
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Bayesian Networks and Decision Graphs
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Event-driven architecture based on patterns for detecting complex attacks
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Events, neural systems and time series
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This paper describes an approach to detect unknown event patterns. In this context, an event is not only something that happens, but also something that can be analysed. This task is processed by a Complex Event Processing (CEP) engine. CEP is an emerging technology for detecting known patterns of events and correlating them to complex events in real-time. In order to reach the goal of finding unknown patterns, several known detection algorithms are discussed. In our work, we focus on discriminant analysis used for recognizing unknown patterns for the use case of credit card transactions and the fraud problem connected with this kind of payment. It is necessary to develop new methods of fraud detection and prevention because of the negative impacts for vendors and customers caused by credit card fraudsters at present. At the same time we would like to make provisions for the more sophisticated fraud methods that will occur in the future.