Reliability Analysis of a Mixed Redundant Model in C 3I System

Aim To define a mixed redundant model(MRM), improving the reliability of C 3I system. Methods The model combined the technology characters of two? unit system with one warm stand by unit and function substitute system. The reliability and availability equations of MRM were deduced. Results and Conclusion Compared with several other reliability models, it has obvious effect upon improving the system reliability. The effect? cost rate is very high among these models. The model can be used in reliability design, evaluation and check of C 3I system. Only a little attached cost is needed to improve C 3I system reliability effectively.

Pattern Analysis and Regressive Linear Measure for Botnet Detection

Capturing the distributed platform with remotely controlled compromised machines using botnet is extensively analyzed by various researchers.However,certain limitations need to be addressed efficiently.The provisioning of detection mechanism with learning approaches provides a better solution more broadly by saluting multi-objective constraints.The bots’patterns or features over the network have to be analyzed in both linear and non-linear manner.The linear and non-linear features are composed of high-level and low-level features.The collected features are maintained over the Bag of Features(BoF)where the most influencing features are collected and provided into the classifier model.Here,the linearity and non-linearity of the threat are evaluated with Support Vector Machine(SVM).Next,with the collected BoF,the redundant features are eliminated as it triggers overhead towards the predictor model.Finally,a novel Incoming data Redundancy Elimination-based learning model(RedE-L)is built to classify the network features to provide robustness towards BotNets detection.The simulation is carried out in MATLAB environment,and the evaluation of proposed RedE-L model is performed with various online accessible network traffic dataset(benchmark dataset).The proposed model intends to show better tradeoff compared to the existing approaches like conventional SVM,C4.5,RepTree and so on.Here,various metrics like Accuracy,detection rate,Mathews Correlation Coefficient(MCC),and some other statistical analysis are performed to show the proposed RedE-L model's reliability.The F1-measure is 99.98%,precision is 99.93%,Accuracy is 99.84%,TPR is 99.92%,TNR is 99.94%,FNR is 0.06 and FPR is 0.06 respectively.

Specifying redundancy tactics as crosscutting concerns using aspect-oriented modeling

Various redundancy tactics can be modeled at the design stage of safety-critical systems thereby providing a set of fault-tolerance guidelines for subsequent development activities. However, existing approaches usually interweave redundancy tactics into the functional models making them complex and cluttered; the maintenance of such models is time-consuming and error-prone. To address this problem, we provide a modeling approach to separate the redundancy tactics from the base functional models using aspect-oriented modeling. More specifically, the conceptual models of the redundancy tactics and their semantic constraints are first defined for deriving the relevant aspects. Subsequently, a UML profile is proposed to specify the tactic aspects followed by mapping these concepts to the corresponding concepts of aspect-oriented modeling based on pre-defined principles. In accordance with our proposed profile, reuse directives are applied to handle the overlap of structural features between redundancy tactics and other kinds of tactic. Based on our tactic aspects and their configured attributes, a weaving algorithm is proposed to associate the tactic aspects with the base functional models. The proposed approach is compared with a traditional tactic modeling approach using two safety-critical systems, revealing that： 1） our approach significantly reduces the number of extra model elements needed in the tactic design stage; 2） our approach can largely avoid the impact of changing of the base functional model as the model evolves.