Feature Object Extraction - Evidence Accrual Applied to Information Assurance and Other Problems

#feature #object #extraction; #information #assurance; #evidence #accrual; #cyber #security; #distinguished #lecture
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AESS Chapter Meeting --Joint with Computer and Computational Intelligence Chapters. Virtual Meeting Registration open to all IEEE chapters and sections to co-host and serve their local membership and the public.

Information assurance, also referred to as cyber security, is the process of protecting information from theft, destruction, or manipulation. Cyber threats can be either from internal or external sources, sudden or taking time to develop, such as a slow denial of service (DOS) attack. Some techniques have been developed to behave as sensors to quickly assess elements of attacks that rely on a decision engine to fuse the information to estimate whether or not an attack is underway. Interpreting cybersecurity as a sensor fusion problem, includes a number of additional alternative techniques into the solution space. The concept of evidence accrual is to gather measurements over time from different sensors to provide estimates of what event is occurring.  A classification fusion technique using feature extraction and fuzzy logic known as Feature Object Extraction is developed and applied to problems such as cyber security and GPS attacks.  The feature-aided object extraction technique was developed for the classification problem to fuse different features and generate both a classification and a measure of the quality of the classification estimate.  A primary advantage of this is that it evidence is built for each possibility without excluding classes.  Thus, the evidence may point to multiple possibilities until evidence disproves a class.  Most probabilistic techniques increase the probability of one class by lowering the probability on other classes.  Another difference exists in the fact that evidence can be applied to individual classes and not all classes.  Feature Object Extraction also allows for a level of evidence to recover from erroneous negative information which might normally cause elimination of a possibility.  These design features of Feature Object Extraction are applied to the cybersecurity problem where multiple attacks might be underway simultaneously.


  Date and Time

  Location

  Hosts

  Registration


  • Date: 24 Jun 2020
  • Time: 05:50 PM to 07:50 PM
  • All times are (GMT-08:00) US/Pacific
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  • (Virtual Meeting)
  • Connection information will be supplied to all preregistered.
  • San Diego, California
  • United States 92130
  • Contact Event Host

  • This meeting will be offered virtually -- IEEE WebEx connection information will be provided to all preregistered attendees. 

  • Co-sponsored by San Diego Chapter of the Aerospace Electronic Systems Society, Computer Chapter, and Computational Intelligence Chapter
  • Starts 06 June 2020 08:50 AM
  • Ends 24 June 2020 07:00 PM
  • All times are (GMT-08:00) US/Pacific
  • Admission fee

  Speakers

Dr. Stephen Stubberud of Oakridge Technology

Biography:

Dr. Stephen Stubberud received his doctorate from the University of California Santa Barbara in Electrical Engineering. He has over twenty years of experience since then working in the fields of sensor data fusion and neural networks. He has worked on a number of fusion problems that include land, sea, and air tracking. His work has in multiplatform tracking systems gave him first-hand knowledge of sensor registration and techniques that exist to compensate for such errors. In the past ten years, Dr. Stubberud has researched techniques to compensate for registration and techniques to associate tracks from different platforms when they are registered. Dr. Stubberud has over 100 publications in the literature. He has worked for ORINCON, Raytheon Space and Airborne System, Boeing, and General Atomics. He is currently the senior research scientist at Oakridge Technology. Dr. Stubberud is a senior member of the IEEE.

Email:

Address:San Diego, California, United States

Prof. Kathleen A. Kramer of University of San Diego

Biography:

Prof. Kathleen A. Kramer is a Professor of Electrical Engineering at the University of San Diego. She received her doctorate in electrical engineering from the California Institute of Technology. She maintains an active research agenda and has over 100 publications in the areas of multi-sensor data fusion, intelligent systems, and neural and fuzzy systems. She has been a Member of Technical Staff at several companies, including ViaSat, Hewlett Packard, and Bell Communications Research. She is a Distinguished Lecturer and a member of the Board of Governors of the IEEE Aerospace Electronic Systems Society.

Email:

Address:San Diego, California, United States, 92130





Agenda

5:50 Virtual Networking with AESS Chapter

6:00 Speakers and Progra

 


Pre-registration required.  This meeting will be available via webex and your contact details are necessary to provide connection information.

This meeting is presented as a preview for a keynote at the IEEE International Conference on Electronics, Computing and Communication Technologies, CONECCT 2020 during JULY 2-4,2020.  https://ieee-conecct.org