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CAE Forum: Stream Data Mining and Applications: Security Perspective

November 4 @ 10:00 am - 11:00 am

Presenter(s): Dr. Latifur Khan, University of Texas at Dallas

Data streams are continuous flows of data. Examples include network traffic, sensor data, and call center
records. Data streams demonstrate several unique properties that together conform to the characteristics of big data (i.e., volume, velocity, variety, and veracity) and add challenges to data stream mining. In this talk we will present an organized picture on how to handle various data mining techniques in data streams. Most existing data stream classification techniques ignore one important aspect of stream data: arrival of a novel class.

In this talk, we address this issue and propose a data stream classification technique that integrates a novel class
detection mechanism into traditional classifiers, enabling automatic detection of novel classes before the true labels of the novel class instances arrive. Novel class detection problem becomes more challenging in the presence of concept-drift, when the underlying data distributions evolve in streams. In this talk we will demonstrate how to make fast and correct classification decisions under this constraint. Furthermore, we will present a semi supervised framework which exploits change detection on classifier confidence values to update the classifier intelligently with limited labeled training data. We will present a number of stream classification applications such as website fingerprinting, real time monitoring, evolving insider threat detection, and textual stream classification. This research was funded in part by National Science Foundation, NASA, Air Force Office of Scientific Research, Sandia National Laboratory, and Raytheon.

Click here to join via Zoom


November 4
10:00 am - 11:00 am