Structure Refinement for Vulnerability Estimation Models using Genetic Algorithm Based Model Generators

In this paper, a method for model structure refinement is proposed and applied in estimation of cumulative number of vulnerabilities according to time. Security as a quality characteristic is presented and defined. Vulnerabilities are defined and their importance is assessed. Existing models used for number of vulnerabilities estimation are enumerated, inspecting their structure. The principles of genetic model generators are inspected. Model structure refinement is defined in comparison with model refinement and a method for model structure refinement is proposed. A case study shows how the method is applied and the obtained results.model structure refinement, model generators, gene expression programming, software vulnerabilities, performance criteria, software metrics

Introduction to the GiNaC Framework for Symbolic Computation within the C++ Programming Language

The traditional split-up into a low level language and a high level language in the design of computer algebra systems may become obsolete with the advent of more versatile computer languages. We describe GiNaC, a special-purpose system that deliberately denies the need for such a distinction. It is entirely written in C++ and the user can interact with it directly in that language. It was designed to provide efficient handling of multivariate polynomials, algebras and special functions that are needed for loop calculations in theoretical quantum field theory. It also bears some potential to become a more general purpose symbolic package

An integrated search-based approach for automatic testing from extended finite state machine (EFSM) models

This is the post-print version of the Article - Copyright @ 2011 ElsevierThe extended finite state machine (EFSM) is a modelling approach that has been used to represent a wide range of systems. When testing from an EFSM, it is normal to use a test criterion such as transition coverage. Such test criteria are often expressed in terms of transition paths (TPs) through an EFSM. Despite the popularity of EFSMs, testing from an EFSM is difficult for two main reasons: path feasibility and path input sequence generation. The path feasibility problem concerns generating paths that are feasible whereas the path input sequence generation problem is to find an input sequence that can traverse a feasible path. While search-based approaches have been used in test automation, there has been relatively little work that uses them when testing from an EFSM. In this paper, we propose an integrated search-based approach to automate testing from an EFSM. The approach has two phases, the aim of the first phase being to produce a feasible TP (FTP) while the second phase searches for an input sequence to trigger this TP. The first phase uses a Genetic Algorithm whose fitness function is a TP feasibility metric based on dataflow dependence. The second phase uses a Genetic Algorithm whose fitness function is based on a combination of a branch distance function and approach level. Experimental results using five EFSMs found the first phase to be effective in generating FTPs with a success rate of approximately 96.6%. Furthermore, the proposed input sequence generator could trigger all the generated feasible TPs (success rate = 100%). The results derived from the experiment demonstrate that the proposed approach is effective in automating testing from an EFSM

Data-driven Estimation of the Power Grid Inertia with Increased Levels of Renewable Generation Resources

The thesis investigates methods for estimating inertia in systems at different levels of renewable energy penetrations. Estimating renewable generators\u27 inertia is challenging because their structures differ from traditional generators. Moreover, the power generated from renewable energy resources is not stable, depending on weather conditions. When a power grid has a disturbance, photovoltaic inverter control influences a power grid inertia by different controllers, such as power factor and reactive power control, to bring a power grid back to a steady state. The changing reactive power impacts the frequency, which strongly relates to inertia and increases the inertia estimation problem. Several papers proposed different approaches to estimating renewable generators\u27 inertia. The two main categories of estimating inertia are model-based and measurement-based methods. The model-based methods mimic an actual renewable generator behavior to calculate inertia. It is a complicated model specialized for specific renewable devices, but unlike the measurement-based methods, it can estimate the inertia in the steady state. The measurement-based methods find the patterns in measured data and use classification or regression functions to calculate inertia. A measurement model can monitor a power grid in real time. However, the method needs parameter oscillation, representing power imbalance in a power grid. This thesis proposes three measurement-based models to estimate inertia for systems under levels of photovoltaic systems: Symbolic Aggregate Approximation, Back Propagation Neural Network, and Minimum Volume Enclosing with a Gradient Descent Machine Model. The measurement-based inertia estimation models need large-scale system measurement data. PowerWorld Simulator has a function to analyze the transient stability, which is utilized in this thesis to generate simulated data for this. Reducing photovoltaic output power can mimic the impact of weather changes. Different types of photovoltaic controllers have various behavior. The Symbolic Aggregate Approximation transfers continuous data into discrete data. The advantage of this method over other techniques is its ability to compress large-scale data and the reduced data storage requirements. Hence, the model demonstrates the best performance for estimating the inertia. The Minimum Volume Enclosing Ellipsoid visualizes measurement data, including frequency, generator output power, and bus voltage, on a 3-dimensional space. The volume of the enclosed ellipsoid is the output that yields label inertia. During a fault in a power system, the volume of the ellipsoid increases. The Gradient Descent Model estimates an optimal regression curve to match volume with label inertia as the estimated inertia. The Back Propagation Neural Network is a nonlinear classification method. With multiple layers and neurons, this method can efficiently cluster complex input features, such as the frequency of all buses and generator output power. The error between the estimated inertia and the label inertia is used to modify the branches\u27 weight to reduce error. The disadvantage of the second and third models is that they do not have a better performance than the first one

The Neighborhood-Level Association Between Alcohol Outlet Density and Female Criminal Victimization Rates

The aim of this study was to explore the neighborhood-level association between alcohol outlet density and non–intimate partner violent victimization rates among females. Violent offending and victimization are more prevalent for males than females, and most research on alcohol outlets and violence emphasizes males. Studies that do focus on alcohol outlets and female violent victimization tend to focus on intimate partner violence (IPV), yet non-IPV events are over three quarters of all female violent victimization incidents in the United States. We collected data on violent victimization rates, on- and off-premise alcohol outlet density, and neighborhood-level covariates of violence rates for Milwaukee block groups. We used spatially lagged regression models to test this association, to compare non-IPV results with those for overall female violent victimization rates, and to compare results for females with those for males. Our findings showed density of both on- and off-premise alcohol outlets was positively associated with non-IPV female violent victimization rates, which is an important finding given lack of research on this topic. We also found results for females (both overall and non-IPV violent victimization) were generally the same as for males, but the effect of off-premise outlets on non-IPV female violent victimization rates was weaker than the same association for males. Our findings have clear policy implications for local jurisdictions. Alcohol outlet density is important for both female and male violent victimization. Limiting the licensing of alcohol-selling establishments, especially those that engage in irresponsible retail practices, may be a suitable approach to address violent victimization