Application of Relevance Vector Machines in Real Time Intrusion Detection

Application of Weighted Support Vector Machines to Network Intrusion Detection

Support Vector Machines(SVMs) have succeeded in many classification fields. Some researchers have tried to apply SVMs to Intrusion Detection recently and got desirable results. By analyzing C-SVM theoretically and experimentally, we found that C-SVM had some properties which showed C-SVM was not most suitable for Network Intrusion Detection. First, C-SVM has different classification error rates...

Real Time Intrusion Detection

Real - Time Intrusion Detection

Distributed mission critical systems require support for ultra-secure communication, in which intrusions must be detected and suppressed in real time, possibly before the aaected messages reach the receiver. When the distributed application has real-time requirements , the eeects of intrusion are particularly severe. In addition to covered channels and potentially tampered data at the receiver,...

Real-time Facial Emotion Detection using Support Vector Machines

There have been continuous researches in the field of emotion detection through faces of biological species in the last few decades. This was further fuelled by the rise of artificial intelligence which has added a new paradigm to its ongoing research. This paper discusses the role of one of the artificial intelligence techniques, Support vector machines for efficient emotion detection. This st...

Supporting a Real-Time Distributed Intrusion Detection Application on GATES

Increasingly, a number of applications across computer sciences and other science and engineering disciplines rely on, or can potentially benefit from, analysis and monitoring of data streams. We view the problem of flexible and adaptive processing of distributed data streams as a grid computing problem. In our recent work, we have been developing a middleware, GATES (Grid-based AdapTive Execut...