Reliability prediction in model driven development

Evaluating the implications of an architecture design early in the software development lifecycle is important in order to reduce costs of development. Reliability is an important concern with regard to the correct delivery of software system service. Recently, the UML Profile for Modeling Quality of Service has defined a set of UML extensions to represent dependability concerns (including reliability) and other non-functional requirements in early stages of the software development lifecycle. Our research has shown that these extensions are not comprehensive enough to support reliability analysis for model-driven software engineering, because the description of reliability characteristics in this profile lacks support for certain dynamic aspects that are essential in modeling reliability. In this work, we define a profile for reliability analysis by extending the UML 2.0 specification to support reliability prediction based on scenario specifications. A UML model specified using the profile is translated to a labelled transition system (LTS), which is used for automated reliability prediction and identification of implied scenarios; the results of this analysis are then fed back to the UML model. The result is a comprehensive framework for addressing software reliability modeling, including analysis and evolution of reliability predictions. We exemplify our approach using the Boiler System used in previous work and demonstrate how reliability analysis results can be integrated into UML models

Sensitivity Analysis for a Scenario-Based Reliability Prediction Model

As a popular means for capturing behavioural requirements, scenariosshow how components interact to provide system-level functionality.If component reliability information is available, scenarioscan be used to perform early system reliability assessment. Inprevious work we presented an automated approach for predictingsoftware system reliability that extends a scenario specificationto model (1) the probability of component failure, and (2) scenariotransition probabilities. Probabilistic behaviour models ofthe system are then synthesized from the extended scenario specification.From the system behaviour model, reliability predictioncan be computed. This paper complements our previous work andpresents a sensitivity analysis that supports reasoning about howcomponent reliability and usage profiles impact on the overall systemreliability. For this purpose, we present how the system reliabilityvaries as a function of the components reliabilities and thescenario transition probabilities. Taking into account the concurrentnature of component-based software systems, we also analysethe effect of implied scenarios prevention into the sensitivity analysisof our reliability prediction technique

Collective Phase Chaos in the Dynamics of Interacting Oscillator Ensembles

We study chaotic behavior of order parameters in two coupled ensembles of self-sustained oscillators. Coupling within each of these ensembles is switched on and off alternately, while the mutual interaction between these two subsystems is arranged through quadratic nonlinear coupling. We show numerically that in the course of alternating Kuramoto transitions to synchrony and back to asynchrony, the exchange of excitations between two subpopulations proceeds in such a way that their collective phases are governed by an expanding circle map similar to the Bernoulli map. We perform the Lyapunov analysis of the dynamics and discuss finite-size effects.Comment: 19 page

Self-Emerging and Turbulent Chimeras in Oscillator Chains

We report on a self-emerging chimera state in a homogeneous chain of nonlocally and nonlinearly coupled oscillators. This chimera, i.e. a state with coexisting regions of complete and partial synchrony, emerges via a supercritical bifurcation from a homogeneous state and thus does not require preparation of special initial conditions. We develop a theory of chimera basing on the equations for the local complex order parameter in the Ott-Antonsen approximation. Applying a numerical linear stability analysis, we also describe the instability of the chimera and transition to a phase turbulence with persistent patches of synchrony

Two Scenarios of Breaking Chaotic Phase Synchronization

Two types of phase synchronization (accordingly, two scenarios of breaking phase synchronization) between coupled stochastic oscillators are shown to exist depending on the discrepancy between the control parameters of interacting oscillators, as in the case of classical synchronization of periodic oscillators. If interacting stochastic oscillators are weakly detuned, the phase coherency of the attractors persists when phase synchronization breaks. Conversely, if the control parameters differ considerably, the chaotic attractor becomes phase-incoherent under the conditions of phase synchronization break.Comment: 8 pages, 7 figure

Partially integrable dynamics of hierarchical populations of coupled oscillators

We consider oscillator ensembles consisting of subpopulations of identical units, with a general heterogeneous coupling between subpopulations. Using the Watanabe-Strogatz ansatz we reduce the dynamics of the ensemble to a relatively small number of dynamical variables plus constants of motion. This reduction is independent of the sizes of subpopulations and remains valid in the thermodynamic limits. The theory is applied to the standard Kuramoto model and to the description of two interacting subpopulations, where we report a novel, quasiperiodic chimera state.Comment: 4 pages, 1 figur

DeepAPT: Nation-State APT Attribution Using End-to-End Deep Neural Networks

In recent years numerous advanced malware, aka advanced persistent threats (APT) are allegedly developed by nation-states. The task of attributing an APT to a specific nation-state is extremely challenging for several reasons. Each nation-state has usually more than a single cyber unit that develops such advanced malware, rendering traditional authorship attribution algorithms useless. Furthermore, those APTs use state-of-the-art evasion techniques, making feature extraction challenging. Finally, the dataset of such available APTs is extremely small. In this paper we describe how deep neural networks (DNN) could be successfully employed for nation-state APT attribution. We use sandbox reports (recording the behavior of the APT when run dynamically) as raw input for the neural network, allowing the DNN to learn high level feature abstractions of the APTs itself. Using a test set of 1,000 Chinese and Russian developed APTs, we achieved an accuracy rate of 94.6%