The structure of commodity operating systems kernels remains largely unchanged despite radical changes in underlying hardware and security risks. Existing research has managed to increase overall monolithic kernel security using various defense mechanisms, such as kernel control-flow integrity, and through the use of active vulnerability discovery techniques such as system call fuzzing. However, these mitigation mechanisms often focus on a class of vulnerabilities while failing to address the broader, underlying architectural issues which amplify the impact of these issues. This paper presents a novel architectural approach that aims to increase the robustness and security of monolithic operating system kernels. We propose an operating system model which focuses on strict decomposition and runtime separation between individual monolithic kernel subsystems through separate execution contexts. We propose a novel, SMP-capable nested kernel architecture that enforces separation policies in an effective, efficient and mechanism-agnostic manner, complemented by a special compiler pass and a domain-specific language that provides a handy and intuitive way of specifying separation policies and automating their integration. We implement a prototype system based on the FreeBSD operating system and the Clang/LLVM compiler. We run a series of intense benchmarks to evaluate our model and separation mechanisms.

Crossover is the most important operator in real-coded genetic algorithms. However, the choice of the best operator for a specific problem can be a difficult task. In this paper we compare 16 crossover operators on a set of 24 benchmark functions. A detailed statistical analysis is performed in an effort to find the best performing operators. The results show that there are significant differences in efficiency of different crossover operators, and that the efficiency may also depend on the distinctive properties of the fitness function. Additionally, the results point out that the combination of crossover operators yields the best results.

This paper investigates the influence of different crossover operators on the efficiency of the hybrid Taguchi genetic algorithm and aims to provide guidelines for algorithm's usage in continuous optimization. We examine the hybrid Taguchi genetic algorithm (HTGA) with 8 different crossover operators and apply it to 15 benchmark numerical optimization problems. The implementation uses binary representation which maps chromosomes to values in real domain with arbitrary precision. Different crossover operators are used with the HTGA and a detailed statistical analysis is performed to evaluate their performance. The results indicate that the HTGA obtains better results with crossover operators different than the ones commonly reported in literature.