Biblio
We consider some approaches to the construction of lightweight block ciphers and introduce the definitions for "index of strong nonlinearity" and "index of perfection". For PRESENT, MIDORI, SKINNY, CLEFIA, LILLIPUT mixing and nonlinear properties were evaluated. We obtain the exact values of the exponents for mixing matrices of round functions and the upper bounds for indexes of perfection and strong nonlinearity. It was determined by the experiment that each coordinate function of output block is nonlinear during 500 rounds. We propose the algorithmic realization of 16×16 S-box based on the modified additive generator with lightweight cipher SPECK as a modification which does not demand memory for storage huge substitution tables. The best value of the differential characteristic of such S-box is 18/216, the minimal nonlinearity degree of coordinate functions is equal to 15 and the minimal linear characteristic is 788/215.
Physical consequences to power systems of false data injection cyber-attacks are considered. Prior work has shown that the worst-case consequences of such an attack can be determined using a bi-level optimization problem, wherein an attack is chosen to maximize the physical power flow on a target line subsequent to re-dispatch. This problem can be solved as a mixed-integer linear program, but it is difficult to scale to large systems due to numerical challenges. Three new computationally efficient algorithms to solve this problem are presented. These algorithms provide lower and upper bounds on the system vulnerability measured as the maximum power flow subsequent to an attack. Using these techniques, vulnerability assessments are conducted for IEEE 118-bus system and Polish system with 2383 buses.
We consider a class of robust optimization problems that we call “robust-to-dynamics optimization” (RDO). The input to an RDO problem is twofold: (i) a mathematical program (e.g., an LP, SDP, IP, etc.), and (ii) a dynamical system (e.g., a linear, nonlinear, discrete, or continuous dynamics). The objective is to maximize over the set of initial conditions that forever remain feasible under the dynamics. The focus of this paper is on the case where the optimization problem is a linear program and the dynamics are linear. We establish some structural properties of the feasible set and prove that if the linear system is asymptotically stable, then the RDO problem can be solved in polynomial time. We also outline a semidefinite programming based algorithm for providing upper bounds on robust-to-dynamics linear programs.