Development of a fuzzy GERT-model for investigating common software vulnerabilities

This paper has determined the relevance of the issue related to improving the accuracy of the results of mathematical modeling of the software security testing process. The fuzzy GERT-modeling methods have been analyzed. The necessity and possibility of improving the accuracy of the results of mathematical formalization of the process of studying software vulnerabilities under the conditions of fuzziness of input and intermediate data have been determined. To this end, based on the mathematical apparatus of fuzzy network modeling, a fuzzy GERT model has been built for investigating software vulnerabilities. A distinctive feature of this model is to take into consideration the probabilistic characteristics of transitions from state to state along with time characteristics. As part of the simulation, the following stages of the study were performed. To schematically describe the procedures for studying software vulnerabilities, a structural model of this process has been constructed. A "reference GERT model" has been developed for investigating software vulnerabilities. The process was described in the form of a standard GERT network. The algorithm of equivalent transformations of the GERT network has been improved, which differs from known ones by considering the capabilities of the extended range of typical structures of parallel branches between neighboring nodes. Analytical expressions are presented to calculate the average time spent in the branches and the probability of successful completion of studies in each node. The calculation of these probabilistic-temporal characteristics has been carried out in accordance with data on the simplified equivalent fuzzy GERT network for the process of investigating software vulnerabilities. Comparative studies were conducted to confirm the accuracy and reliability of the results obtained. The results of the experiment showed that in comparison with the reference model, the fuzziness of the input characteristic of the time of conducting studies of software vulnerabilities was reduced, which made it possible to improve the accuracy of the simulation results.

A fuzzy sensitivity analysis approach to estimate brain effective connectivity and its application to epileptic seizure detection

The brain is considered to be the most complicated organ in human body. Inferring and quantification of effective (causal) connectivity among regions of the brain is an important step in characterization of its complicated functions. The proposed method is comprised of modeling multivariate time series with Adaptive Neurofuzzy Inference System (ANFIS) and carrying out a sensitivity analysis using Fuzzy network parameters as a new approach to introduce a connectivity measure for detecting causal interactions between interactive input time series. The results of simulations indicate that this method is successful in detecting causal connectivity. After validating the performance of the proposed method on synthetic linear and nonlinear interconnected time series, it is applied to epileptic intracranial Electroencephalography (EEG) signals. The result of applying the proposed method on Freiburg epileptic intracranial EEG data recorded during seizure shows that the proposed method is capable of discriminating between the seizure and non-seizure states of the brain.

Criteria for comparing artificial intelligence systems

Модели нейронных и нейро-нечетких сетевых критериев сравнения в задачах диагностики и классификации образов. Предложен комплекс критериев для оценки свойств искусственных нейронных и нейро-нечетких сетей. Он включает в себя критерии разнообразия, подгонки, эластичности, равнозначности, устойчивости к шуму, аварийной ситуации, а также заданную монотонность для построения нейронной модели. Применение предложенных критериев на практике позволяет автоматизировать процесс построения, анализа и сравнения нейронных моделей для решения задач диагностики и классификации паттернов. Предложено решение задачи повышения эффективности параметрического синтеза нейросетевых моделей сложных систем для обоснованного принятия решений о классификации подводных целей. Научная новизна работы заключается в том, что впервые предложен комплекс моделей критериев, характеризующих такие свойства нейронных и нейро-нечетких сетей как разнообразие, переобученность, эластичность, эквифинальность, устойчивость к шуму, эмерджентность, что позволяет автоматизировать решение задачи анализа свойств и сравнения нейросетевых и нейро-нечетких моделей при решении задач диагностики и классификации образов. В работе решена актуальная задача автоматизации анализа свойств и сравнения нейросетевых моделей. Models of neural and neuro-fuzzy network comparison criterions in the tasks of diagnostics and pattern classification. The complex of criterions for an estimation of properties artificial neural and neuro-fuzzy networks is proposed. It includes criterions of variety, overfitting, elasticity, equifinality, stability to a noise, emergency, and also set monotonicity for a neural model construction. The application of offered criterions in practice allows to automatize the process of a construction, analysis and comparison of neural models for problem solving of diagnostics and patternt classification. The solution of the problem of increasing the efficiency of parametric synthesis of neural network models of complex systems for informed decision-making on the classification of underwater targets is proposed. The scientific novelty of the work lies in the fact that for the first time a set of models of criteria characterizing such properties of neural and neuro-fuzzy networks as diversity, retraining, elasticity, equifinality, noise resistance, emergence is proposed, which allows automating the solution of the problem of analyzing the properties and comparing neural network and neuro-fuzzy models when solving problems of diagnostics and classification of images. The paper solves the actual problem of automating the analysis of properties and comparison of neural network models.

Assessment the collision risk level for an autonomous unmanned ship involving fuzzy logic and neural network technologies

В работе предлагается решение задачи по оценке степени опасности столкновения с надводными объектами на море для автономного безэкипажного судна. Такая оценка в предлагаемой модели основывается на двух параметрах: ожидаемая дистанция и время кратчайшего сближения автономного безэкипажного судна с другими объектами, которые могут встретиться на траектории его движения. Разработана и описана модель на базе теории нечётких множеств, демонстрирующая удовлетворительное качество оценки степени опасности. Также в работе представлена усовершенствованная модель с применением теории искусственных нейронных сетей – адаптивная нейро-нечеткая сеть, улучшающая качество оценки степени опасности более чем на два порядка. В работе показаны результаты оптимизации параметров адаптивной нейро-нечёткой сети, обеспечивающей качественную оценку степени опасности столкновения безэкипажного судна, также представлены рассчитанные оценки качества как для нечёткой модели, так и для наилучшей нейро-нечёткой модели. The paper proposes a solution of the problem of assessing the collision risk level with surface objects at sea for an autonomous unmanned ship. Such collision risk level asses in the proposed model is based on two parameters: the distance at closest point of approach and the time to closest point of approach for autonomous unmanned ship with other marine surface objects that may meet on the trajectory of its movement. In this work developed and described model based on the theory of fuzzy sets, which demonstrates the satisfactory quality of assessing the collision risk level. The paper also presents an improved model using the theory of artificial neural networks - an adaptive neuro-fuzzy network that improves the assessment quality of collision risk level by more than hundredfold. The paper shows the results of optimization of the parameters of an adaptive neuro-fuzzy network, which provides a qualitative assessment of collision risk level for an unmanned ship, and presents the calculated quality estimates for both a fuzzy and the neuro-fuzzy model.

COMPARATIVE STUDY OF MPPT BASED ON ARTIFICIAL INTELLIGENCE

An MPPT or Maximum power point tracking command, associated with an intermediate adaptation stage, allows a photovoltaic generator (GPV) to operate in such a way as to continuously produce the maximum of its power. We present in this paper a new intelligent approach of a MPPT based on the hybrid and adaptive neuro-fuzzy network of ANFIS model. The latter is applied to a SEPIC* converter in order to extract at any time the maximum power available at the generator terminals and transfer it into the load, regardless of the sunshine variation as well as the temperature. The proposed method for a fixed and simple structure implements a Takagi-sugeno fuzzy system. Its performance will be confirmed by the comparison with the fuzzy logic command which is already known with its speed.