MASTER OF SCIENCE PROJECT DEFENSE BY: Abigail E. O'Brien
When: Tuesday,
April 26, 2016
11:00 AM
-
1:00 PM
Where: > See description for location
Cost: Free
Description: TOPIC: QUANTIFYING SYSTEM SECURITY THROUGH MARKOV CHAINS AND RELATED STOCHASTIC MODELS FOR RELIABILITY
LOCATION: College of Engineering Conference Room, Textile's Building - Room 101E
ABSTRACT:
Network security is a heavily researched field with an endless cycle of newly discovered attacks and methods to mitigate them, but with very few models to numerically represent the resultant effect. When the security of a system can be quantified, values such as its marketability and the outcome of improved encryption algorithms become easier to visualize. This project aims to investigate stochastic models for reliability analysis and apply them in a way that can distinctively measure the desired security attributes (confidentiality, integrity, availability) of a specific system. Particularly, Markov chains, binary decision diagrams and related probability-based approaches are implemented for overall system security evaluation. The methods are demonstrated through a detailed case study of a body area network, which consists of several wearable computing devices used for health monitoring.
NOTE: All ECE Graduate Students are ENCOURAGED to attend.
All interested parties are invited to attend. Open to the public.
Advisor: Dr. Liudong Xing
Committee Members: Dr. Hong Liu and Dr. Honggang Wang, Department of Electrical & Computer Engineering
*For further information, please contact Dr. Liudong Xing at 508.999.8883, or via email at lxing@umassd.edu.
LOCATION: College of Engineering Conference Room, Textile's Building - Room 101E
ABSTRACT:
Network security is a heavily researched field with an endless cycle of newly discovered attacks and methods to mitigate them, but with very few models to numerically represent the resultant effect. When the security of a system can be quantified, values such as its marketability and the outcome of improved encryption algorithms become easier to visualize. This project aims to investigate stochastic models for reliability analysis and apply them in a way that can distinctively measure the desired security attributes (confidentiality, integrity, availability) of a specific system. Particularly, Markov chains, binary decision diagrams and related probability-based approaches are implemented for overall system security evaluation. The methods are demonstrated through a detailed case study of a body area network, which consists of several wearable computing devices used for health monitoring.
NOTE: All ECE Graduate Students are ENCOURAGED to attend.
All interested parties are invited to attend. Open to the public.
Advisor: Dr. Liudong Xing
Committee Members: Dr. Hong Liu and Dr. Honggang Wang, Department of Electrical & Computer Engineering
*For further information, please contact Dr. Liudong Xing at 508.999.8883, or via email at lxing@umassd.edu.
Contact:
ECE: Electrical & Computer Engineering Department 508.999.9164 http://www.umassd.edu/engineering/ece/
Topical Areas: Faculty, General Public, University Community, College of Engineering, Electrical and Computer Engineering