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SCS’09 TUTORIAL SESSIONS Half Day Tutorial 1 Abstract: This tutorial discusses the specific design constraints related to secure integrated or embedded systems or at least the most critical elements in such systems, e.g. crypto-processors. The main focus is on hardware-based attacks and some possible solutions. After a presentation of the general context, the basics of circuit-level attacks are summarized. Circuit- and architecture-level methods for the design and implementation of robust secure circuits are explained, including manufacturing test concerns. Characteristics and limitations of the main hardware protection schemes (also called counter-measures) are discussed. Experimental attack data are shown on several implementation technologies (ASIC and FPGA). Biography: Régis LEVEUGLE received the PhD degree in Microelectronics from the National Polytechnical Institute of Grenoble (INPG), France, in 1990. He is currently Professor at this institute and member of TIMA laboratory. His main interests are computer architecture, VLSI design methods and CAD tools, dependability evaluation, fault tolerant architectures, concurrent checking and secure circuit design. He has 20 years experience in teaching VLSI design, test and dependability. He has authored or co-authored more than 140 scientific papers and served as a reviewer for many journals and conferences. He has been a member of the French evaluation committee for national projects on security. He has also served on more than 50 international conference program and organization committees. He was Program co-Chair for the 2001 IEEE International Symposium on Defect and Fault Tolerance in VLSI Systems (DFT'01), vice-General Chair for the 2002 IEEE International On-Line Testing Workshop, General co-Chair for DFT'02, vice-Program Chair for the 2003, 2005 and 2007 IEEE International On-Line Testing Symposium (IOLTS) and Program co-Chair for IOLTS'03 and IOLTS'06. He is a member of IEEE. Half Day Tutorial 2 Abstract: The tutorial focuses on tools of computational intelligence; namely fuzzy logic, neural networks and neuro- fuzzy systems, as promising alternatives to compensate for the modeling and parametric uncertainties, which are often associated with highly nonlinear systems. Such systems are usually characterized by complex ill-defined dynamics for which a precise mathematical model is either impossible or prohibitively expensive to achieve. Thus, controlling such systems with conventional control methods under the assumption of a good understanding of their dynamics and operating environments may lead to an unsatisfactory performance and even to instability in some cases. Although the techniques covered in the tutorial are applicable to virtually any system with the aforementioned properties, examples are mainly drawn from the field of robotics.
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