Self-synchronizing stream ciphers and dynamical systems: state of the art and open issues

Dynamical systems play a central role in the design of symmetric cryptosystems. Their use has been widely investigated both in "chaos-based" private communications and in stream ciphers over finite fields. In the former case, they get the form of automata named as Moore or Mealy machines. The main charateristic of stream ciphers lies in that they require synchronization of complex sequences generated by the dynamical systems involved at the transmitter and the receiver part. In this paper, we focus on a special class of symmetric ciphers, namely the SelfSynchronizing Stream Ciphers. Indeed, such ciphers have not been seriously explored so far although they get interesting properties of synchronization which could make them very appealing in practice. We review and compare different design approaches which have been proposed in the open literature and fully-specified algorithms are detailed for illustration purpose. Open issues related to the validation and the implementation of Self-Synchronizing Stream Ciphers are developped. We highlight the reason why some concepts borrowed from control theory appear to be useful to this end

An LPV framework for chaos synchronization in communication

This paper proposes a unified framework to achieve chaos synchronization of both classes of chaotic discrete-time systems, namely maps involving polynomial nonlinearities and piecewise linear maps. It is shown that all of those chaotic systems can be rewritten as a polytopic Linear Parameter Varying (LPV) system. A unified approach to tackle chaos synchronization problems encountered in communication is derived

FLAT DYNAMICAL SYSTEMS AND SELF-SYNCHRONIZING STREAM CIPHERS

Abstract. In this paper, we present properties of dynamical systems and their use for cryptographical applications. In particular, we study the relationship with the self-synchronizing stream ciphers from a structural point of view. Finally a framework involving discrete Lyapunov exponents and Walsh transform is sketched to characterize the dynamical behaviors. 1

On Learning Machines for Engine Control

The original publication is available at www.springerlink.comThe chapter deals with neural networks and learning machines for engine control applications, particularly in modeling for control. In the first section, some basics on the common features of engine control are recalled, based on a layered engine management structure. Then the use of neural networks for engine modeling, control and diagnosis is briefly described. The need for descriptive models for model-based control and the link between physical models and black box models are emphasized at the end of this section by exposing the grey box approach taken in this chapter. The second section introduces the neural models most used in engine control, namely, MultiLayer Perceptrons (MLP) and Radial Basis Function (RBF) networks. A more recent approach, known as Support Vector Regression (SVR), to build models in kernel expansion form is then presented. The third section is devoted to examples of application of these models in the context of turbocharged Spark Ignition (SI) engines with Variable Camshaft Timing (VCT). This specific context is representative of modern engine control problems. In the first example, the airpath control is studied, where open loop neural estimators are combined with a dynamical polytopic observer. The second example considers modeling the in-cylinder residual gas fraction by Linear Programming SVR (LP-SVR), based on a limited amount of experimental data and a simulator built from prior knowledge. Each example tries to show that models based on first principles and neural models must be joined together in a grey box approach to obtain efficient and acceptable results