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Non-intrusive process-based monitoring system to mitigate and prevent VM vulnerability explorations

Non-intrusive process-based monitoring system to mitigate an...

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international conference on Collaborative Computing: Networking, Applications and Worksharing (CollaborateCom)

作者： Chun-Jen Chung JingSong Cui Pankaj Khatkar Dijiang Huang School of Computing Informatics and Decision Systems Engineering Arizona State University Tempe AZ USA Computing School Wuhan University Wuhan China

Cloud is gaining momentum but its true potential is hampered by the security concerns it has raised. Having vulnerable virtual machines in a virtualized environment is one such concern. Vulnerable virtual machines are an easy target and existence of such weak nodes in a network jeopardizes its entire security structure. Resource sharing nature of cloud favors the attacker, in that, compromised machines can be used to launch further devastating attacks. First line of defense in such case is to prevent vulnerabilities of a cloud network from being compromised and if not, to prevent propagation of the attack. To create this line of defense, we propose a hybrid intrusion detection framework to detect vulnerabilities, attacks, and their carriers, i.e. malicious processes in the virtual network and virtual machines. this framework is built on attack graph based analytical models, VMM-based malicious process detection, and reconfigurable virtual network-based countermeasures. the proposed framework leverages software Defined Networking to build a monitor and control plane over distributed programmable virtual switches in order to significantly improve the attack detection and mitigate the attack consequences. the system and security evaluations demonstrate the efficiency and effectiveness of the proposed solution.

关键词： Monitoring Intrusion detection Ports (Computers) software Semantics Malware

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An analysis of multi-objective evolutionary algorithms for training ensemble models based on different performance measures in software effort estimation 13

An analysis of multi-objective evolutionary algorithms for t...

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Proceedings of the 9th international conference on predictive models in software engineering

作者： Leandro L. Minku Xin Yao The University of Birmingham Edgbaston Birmingham UK

ISBN: (纸本)9781450320160

Background: Previous work showed that Multi-objective Evolutionary Algorithms (MOEAs) can be used for training ensembles of learning machines for software Effort Estimation (SEE) by optimising different performance measures concurrently. Optimisation based on three measures (LSD, MMRE and PRED(25)) was analysed and led to promising results in terms of performance on these and other ***: (a) It is not known how well ensembles trained on other measures would behave for SEE, and whether training on certain measures would improve performance particularly on these measures. (b) It is also not known whether it is best to include all SEE models created by the MOEA into the ensemble, or solely the models with the best training performance in terms of each measure being optimised. Investigating (a) and (b) is the aim of this ***: MOEAs were used to train ensembles by optimising four different sets of performance measures, involving a total of nine different measures. the performance of all ensembles was then compared based on all these nine performance measures. Ensembles composed of different sets of models generated by the MOEAs were also ***: (a) Ensembles trained on LSD, MMRE and PRED (25) obtained the best results in terms of most performance measures, being considered more successful than the others. Optimising certain performance measures did not necessarily lead to the best test performance on these particular measures probably due to overfitting. (b) there was no inherent advantage in using ensembles composed of all the SEE models generated by the MOEA in comparison to using solely the best SEE model according to each measure ***: Care must be taken to prevent overfitting on the performance measures being optimised. Our results suggest that concurrently optimising LSD, MMRE and PRED (25) promoted more ensemble diversity than other combinations of measures, and hence performed best. Low diversity is more likely to lea

关键词： ensembles of learning machines

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the impact of parameter tuning on software effort estimation using learning machines 13

The impact of parameter tuning on software effort estimation...

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Proceedings of the 9th international conference on predictive models in software engineering

作者： Liyan Song Leandro L. Minku Xin Yao The University of Birmingham Edgbaston Birmingham UK

ISBN: (纸本)9781450320160

Background: the use of machine learning approaches for software effort estimation (SEE) has been studied for more than a decade. Most studies performed comparisons of different learning machines on a number of data sets. However, most learning machines have more than one parameter that needs to be tuned, and it is unknown to what extent parameter settings may affect their performance in SEE. Many works seem to make an implicit assumption that parameter settings would not change the outcomes ***: To investigate to what extent parameter settings affect the performance of learning machines in SEE, and what learning machines are more sensitive to their ***: Considering an online learning scenario where learning machines are updated with new projects as they become available, systematic experiments were performed using five learning machines under several different parameter settings on three data ***: While some learning machines such as bagging using regression trees were not so sensitive to parameter settings, others such as multilayer perceptrons were affected dramatically. Combining learning machines into bagging ensembles helped making them more robust against different parameter settings. the average performance of k-NN across different projects was not so much affected by different parameter settings, but the parameter settings that obtained the best average performance across time steps were not so consistently the best throughout time steps as in the other ***: Learning machines that are more/less sensitive to different parameter settings were identified. the different sensitivity obtained by different learning machines shows that sensitivity to parameters should be considered as one of the criteria for evaluation of SEE approaches. A good learning machine for SEE is not only one which is able to achieve superior performance, but also one that is either less dependent on parameter settings or to which goo

关键词： ensembles

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Comparing the performance of fault prediction models which report multiple performance measures: Recomputing the confusion matrix 12

Comparing the performance of fault prediction models which r...

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8th international conference on predictive models in software engineering, promise 2012 - Co-located with ESEM 2012

作者： Bowes, David Hall, Tracy Gray, David Science and Technology Research Institute University of Hertfordshire College Lane Hatfield AL10 9AB United Kingdom Department of Information Systems and Computing Brunel University Uxbridge Middlesex UB8 3PH United Kingdom

ISBN: (纸本)9781450312417

there are many hundreds of fault prediction models published in the literature. the predictive performance of these models is often reported using a variety of different measures. Most performance measures are not directly comparable. this lack of comparability means that it is often difficult to evaluate the performance of one model against another. Our aim is to present an approach that allows other researchers and practitioners to transform many performance measures of categorical studies back into a confusion matrix. Once performance is expressed in a confusion matrix alternative preferred performance measures can then be derived. Our approach has enabled us to compare the performance of 600 models published in 42 studies. We demonstrate the application of our approach on several case studies, and discuss the advantages and implications of doing this. Copyright © 2012 ACM.

关键词： Image resolution

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Facilitating DACF by implementation of integrated software solutions

Facilitating DACF by implementation of integrated software s...

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international conference on European Electricity Market, EEM

作者： P. Krstevski J. Angelov R. Taleski A. Krkoleva K. Naumoski A. Paunoski Faculty of Electrical Engineering and Information Technologies Ss Cyril and Methodius University Skopje Macedonia Macedonian Transmission System Operator Skopje Macedonia

the aim of this paper is to present how the Day Ahead Congestion Forecast (DACF) procedure can be facilitated and automated by a specially developed software solution. DACF is a regular procedure for power flow forecast in the European Interconnected network, performed on daily basis (today for tomorrow). the emphasis in this paper is on the first step of the procedure which is creation of regional forecasted network models. the paper presents the structure and the functionalities of the developed solution for network model creation which includes UCTE format data conversion, merging several network models into a single model, generating new models based on user defined inputs and stored network data and comparing different network models of a stored network. the proposed solution can be upgraded with new additional functionalities, which makes it rather flexible for further use.

关键词： Load modeling Data models Load flow predictive models Numerical models Databases software

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think locally, act globally: Improving defect and effort prediction models

Think locally, act globally: Improving defect and effort pre...

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international Mining software Repositories, MSR

作者： Nicolas Bettenburg Meiyappan Nagappan Ahmed E. Hassan Software Analysis and Intelligence Laboratory (SAIL) School of Computing Queen's University Kingston ONT Canada

Much research energy in software engineering is focused on the creation of effort and defect prediction models. Such models are important means for practitioners to judge their current project situation, optimize the allocation of their resources, and make informed future decisions. However, software engineering data contains a large amount of variability. Recent research demonstrates that such variability leads to poor fits of machine learning models to the underlying data, and suggests splitting datasets into more fine-grained subsets with similar properties. In this paper, we present a comparison of three different approaches for creating statistical regression models to model and predict software defects and development effort. Global models are trained on the whole dataset. In contrast, local models are trained on subsets of the dataset. Last, we build a global model that takes into account local characteristics of the data. We evaluate the performance of these three approaches in a case study on two defect and two effort datasets. We find that for both types of data, local models show a significantly increased fit to the data compared to global models. the substantial improvements in both relative and absolute prediction errors demonstrate that this increased goodness of fit is valuable in practice. Finally, our experiments suggest that trends obtained from global models are too general for practical recommendations. At the same time, local models provide a multitude of trends which are only valid for specific subsets of the data. Instead, we advocate the use of trends obtained from global models that take into account local characteristics, as they combine the best of both worlds.

关键词： predictive models Data models Biological system modeling software engineering Measurement Adaptation models software

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Feasibility study for implementing model based predictive control in a Programmable Electronic Device

Feasibility study for implementing model based predictive co...

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international conference on Control and Automation (ICCA)

作者： Miguel R. Celi Cesar De Prada thamara Villegas Daniel Sarabia Asdrubal Aguilera Industrial Technology Department Simón Bolívar University Caracas Venezuela System Engineering and Automatic Control Department University of Valladolid Valladolid Spain Electrical and Electronic Circuit Department Simón Bolívar University Caracas Venezuela

In this paper, the authors studied the implementation issues of an explicit model predictive controller on a programmable electronic device. For the implementation of this controller is considered the programmable controller architecture, hardware and software level, so the performance is optimized ensuring the feasibility of functioning. the main outcome is a series of recommendations to help implement the explicit MPC, which are applied to design the MPC of a DC-DC converter.

关键词： Microcontrollers Process control Performance evaluation Field programmable gate arrays Mathematical model Clocks predictive models

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the system simulation and investigating the distribution laws of functions of random quantities

The system simulation and investigating the distribution law...

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international conference on Actual Problems of Electronic Instrument engineering (APEIE)

作者： B.Y. Lemeshko D.V. Ogurtsov School of Computer Science and Theory Donghua University DHU Shanghai China

Probabilities distribution laws of various functions of random variable, submitting to different one-dimensional distribution laws are investigated by methods of statistical modeling with using the developed software.... 详细信息

关键词： Parameter estimation Robustness Power system modeling Least squares methods Probability distribution Random variables System testing predictive models Equations Statistical analysis

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the Soft Computing Approach to Program Development Time Estimation

The Soft Computing Approach to Program Development Time Esti...

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international conference on Information Technology (ICIT)

作者： Vandana Bhattacherjee Department. of CSE Birla Institute of Technology Ranchi India

software effort modeling is one of the fields in which machine learning techniques have proved effective. In this paper, attempt has been made to establish a relation between program development time with respect to its dependence on various program and personnel attributes. A neural network model has been developed to predict the development time of various software programs. the model is a three layer feed forward network with 17 neurons in the hidden layer. the model has 5 inputs and 1 output.

关键词： Mathematical model Neural networks predictive models Object oriented modeling Neurons Programming profession Testing software engineering Feeds Transfer functions

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Adaptive runtime management of SAMR applications 1

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9th international conference on High Performance Computing (HiPC 2002)

作者： Chandra, S Sinhal, S Parashar, M Zhang, YL Yang, JM Hariri, S Rutgers State Univ Appl Software Syst Lab Piscataway NJ 08854 USA Univ Arizona Dept Elect & Comp Engn Tucson AZ 85721 USA

ISBN: (数字)9783540362654

ISBN: (纸本)3540003037

this paper presents the design, prototype implementation, and evaluation of a runtime management framework for structured adaptive mesh refinement applications. the framework is capable of reactively and proactively managing and optimizing application execution using current system and application state, predictive models for system behavior and application performance, and an agent based control network. the overall goal of this research is to enable large-scale dynamically adaptive scientific and engineering simulations on distributed, heterogeneous and dynamic execution environments such as the computational "grid".

关键词： adaptive runtime management structured adaptive mesh refinement dynamic applications heterogeneous distributed computing performance characterization

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