Concerning the problem that the traditional information leakage detection technology can not effectively detect implicit information leakage in Android applications, a reasoning method of Android Implicit Information Flow (ⅡF) combining control structure ontology model and Semantic Web Rule Language (SWRL) inference rule was proposed. Firstly, the key elements that generate implicit information flow in control structure were analyzed and modeled to establish the control structure ontology model. Secondly, based on the analysis of the main reasons of implicit information leakage, criterion rules of implicit information flow based on Strict Control Dependence (SCD) were given and converted into SWRL inference rules. Finally, control structure ontology instances and SWRL inference rules were imported into the inference engine Jess for reasoning. The experimental results show that the proposed method can deduce a variety of implicit information flow based on SCD with different nature and the testing accuracy of sample set is 83.3%, and the reasoning time is in the reasonable interval when the branch number is limited. The proposed model can effectively assist traditional information leakage detection to improve its accuracy.

Through establishing an appropriate fitness function, the parameter estimation problem for residue hydrofining reaction kinetics model was formulated as a multi-dimensional functional optimization problem, which can be solved by Composite Genetic Algorithm (CGA). Chaotic sequences design method was introduced to construct the initialization population that was scattered uniformly over the entirely search space in order to maintain the diversity. The CGA randomly combined several effective crossover strategies with some suitable mutation strategies at each generation to create new offspring individuals. The simulation results on four benchmark problems demonstrate the effectiveness and robustness of the proposed algorithm. Taking a catalytic cracking unit in oil refinery as an example, a numerical application of the parameter estimation for residue hydrofining reaction kinetics model was solved. Satisfactory results were obtained.