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The positive outcomes of information security awareness training in companies - A case study

Information Security Technical Report 2009年第4期14卷 223-229页

作者： Eminaǧaoǧlu, Mete Uçar, Erdem Eren, Şaban Yaar University Department of Computer Programming Izmir Turkey Trakya University Department of Computer Engineering Edirne Turkey Yaar University Department of Statistics Izmir Turkey

One of the key factors in successful information security management is the effective compliance of security policies and proper integration of "people", "process" and "technology". When it comes to the issue of "people", this effectiveness can be achieved through several mechanisms, one of which is the security awareness training of employees. However, the outcomes should also be measured to see how successful and effective this training has been for the employees. In this study, an information security awareness project is implemented in a company both by training and by subsequent auditing of the effectiveness and success of this training (which focussed on password usage, password quality and compliance of employees with the password policies of the company). The project was conducted in a Turkish company with 2900 white-collar employees. Each employee took information security training including password usage. Also, there were several supporting awareness campaigns such as educational posters, animations and e-messages on the company Intranet, surveys and simple online quizzes. The project was carried out over a 12 month period and three password security strength audits were made during this period. The results were comparatively and statistically analysed. The results show us the effectiveness of the project and the impact of human awareness on the success of information security management programmes in companies. This study gives us some crucial results, facts and methods that can also be used as a guideline for further similar projects. © 2010 Elsevier Ltd. All rights reserved.

关键词： Personnel training

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PATHOPHYSIOLOGICAL EFFECT OF SCARLET FEVER TOXIN .2. EFFECT OF SCARLET FEVER TOXIN ON THE PHAGOCYTIC-ACTIVITY OF THE RETICULOENDOTHELIAL SYSTEM

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FOLIA MICROBIOLOGICA 1979年第5期24卷 415-427页

作者： HRIBALOVA, V Institute of Hygiene and Epidemiology Prague 10 Head of Department of Mathematical Statistics and Programming Institute of Hygiene and Epidemiology Prague Institute of Hygiene and Epidemiology Prague

In normal rabbits and mice, one i.v. injection of scarlet fever toxin (ET) (30,000 STD [skin test doses]/kg of rabbit wt or 20 g mouse) elicited a similar biphasic change in C clearance rate (early depression followed by a stimulating phase) as described for gram-negative endotoxins. Prolonged depression without a subsequent stimulation phase was obtained in mice by raising the ET dose. Pyrogenic tolerance to ET is not accompanied by accelerated C clearance and is not impaired by RES blockade. A possible mechanism of ET tolerance is suggested.

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Implementation and comparison of machine learning classifiers for information security risk analysis of a human resources department

Implementation and comparison of machine learning classifier...

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International Conference on Computer Information Systems and Industrial Management Applications

作者： Eminagaoglu, Mete Eren, Saban Department of Computer Programming Yasar University Izmir Turkey Department of Statistics Yasar University Izmir Turkey

ISBN: (纸本)9781424478170

The aim of this study is threefold. First, a qualitative information security risk survey is implemented in human resources department of a logistics company. Second, a machine learning risk classification and prediction model with proper data set is established from the results obtained in this survey. Third, several classifier algorithms are tested where their training and test performances are compared using error rates, ROC curves, Kappa statistics and F-measures. The results show that some classifier algorithms can be used to estimate specific human based information security risks within acceptable error rates. ©2010 IEEE.

关键词： Surveys

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Deep Learning in IoT: An LSTM Approach for NDVI Forecasting 3

Deep Learning in IoT: An LSTM Approach for NDVI Forecasting

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3rd IEEE International Conference on Electronic Engineering, ICEEM 2023

作者： Sherif, Khaled Azmy, Mohamed Alakkari, Khder Abotaleb, Mostafa El-Kenawy, El-Sayed M. Delta Higher Institute Department of Communications Mansoura Egypt Delta Higher Institute Department of Civil Engineering Mansoura Egypt University of Tishreen Department of Statistics and Programming Faculty of Economics Tartous Syria South Ural State University Department of System Programming Chelyabinsk454080 Russia

ISBN: (纸本)9798350323511

This study presents a novel application of a Long Short-Term Memory (LSTM) deep learning model for time-series analysis of the Normalized Difference Vegetation Index (NDVI) from January 1, 1984, to April 21, 2023. As remote sensing technologies generate substantial environmental data, advanced analytics like LSTM provide essential tools for precise interpretation and forecasting. Through grid search optimization, hyperparameters were fine-tuned for optimal LSTM performance. The NDVI mean value over the study period is 0.332, indicative of a moderate vegetation presence. The data series' sta-tionarity, confirmed through the Dickey-Fuller test, contributes to accurate prediction outcomes. The LSTM model demonstrates superior predictive performance, evidenced by the Root Mean Squared Error (RMSE) values of 0.000764 and 0.000900 for the training and testing datasets respectively. The high R-squared and correlation values further substantiate its efficacy. This study paves the way for leveraging LSTM models in large-scale NDVI data analysis, contributing to environmental monitoring, climate change tracking, and vegetation health assessments. Future work can extend this model to other remote sensing indices and explore various deep learning architectures for enhanced predictive accuracy. The main objective is to identify the optimal LSTM hyperparameters for NDVI prediction using grid search optimization. Our results are expected to provide valuable insights into how LSTM models can be effectively tuned for improved NDVI prediction, potentially benefiting environmental monitoring and decision-making processes. © 2023 IEEE.

关键词： Forecasting

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Forecasting Global Monkeypox Infections Using LSTM: A Non-Stationary Time Series Analysis 3

Forecasting Global Monkeypox Infections Using LSTM: A Non-St...

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3rd IEEE International Conference on Electronic Engineering, ICEEM 2023

作者： Osama, Omnia M. Alakkari, Khder Abotaleb, Mostafa El-Kenawy, El-Sayed M. Delta Higher Institute of Engineering and Technology Department of Communications and Electronics Mansoura Egypt University of Tishreen Faculty of Economics Department of Statistics and Programming Tartous Syria South Ural State University Department of System Programming Chelyabinsk454080 Russia Delta Higher Institute Department of Communications Mansoura Egypt

ISBN: (纸本)9798350323511

This study leverages the capabilities of Long Short-Term Memory (LSTM) models in forecasting global Monkeypox infections, thereby demonstrating the significant potential of advanced machine learning techniques in epidemiological forecasting. Our LSTM model effectively navigates the challenges posed by non-stationary time-series data, a common issue in epidemiological studies. It successfully captures the underlying patterns in the data, producing reliable forecasts. The model's performance was evaluated using several metrics, including RMSE, MSE, MAE, and R2, all of which pointed to its robust and satisfactory predictive capabilities. Our findings underscore the significant role LSTM models can play in informing the development of timely and effective disease control and prevention strategies. They thereby contribute to enhancing public health responses to emerging infectious diseases such as Monkeypox. However, despite the promising results, the study highlights the ongoing challenge of enhancing the interpretability of LSTM models, an area that warrants further research. As a future direction, efforts should focus on refining LSTM models to bolster their interpretability, ensuring their broader adoption and utility in public health practice. © 2023 IEEE.

关键词： Long short-term memory

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ANCESTRAL PROTEIN SEQUENCE RECONSTRUCTION USING A TREE-STRUCTURED ORNSTEIN-UHLENBECK VARIATIONAL AUTOENCODER 10

ANCESTRAL PROTEIN SEQUENCE RECONSTRUCTION USING A TREE-STRUC...

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10th International Conference on Learning Representations, ICLR 2022

作者： Moreta, Lys Sanz Rønning, Ola Al-Sibahi, Ahmad Salim Hein, Jotun Theobald, Douglas Hamelryck, Thomas Probabilistic Programming Group PLTC Section University of Copenhagen Copenhagen Denmark Department of Statistics University of Oxford Oxford United Kingdom Brandeis University Biochemistry Department MA United States Probabilistic Programming Group SCARB PLTC Section Department of Biology Computer Science University of Copenhagen Copenhagen Denmark

We introduce a deep generative model for representation learning of biological sequences that, unlike existing models, explicitly represents the evolutionary process. The model makes use of a tree-structured Ornstein-Uhlenbeck process, obtained from a given phylogenetic tree, as an informative prior for a variational autoencoder. We show the model performs well on the task of ancestral sequence reconstruction of single protein families. Our results and ablation studies indicate that the explicit representation of evolution using a suitable tree-structured prior has the potential to improve representation learning of biological sequences considerably. Finally, we briefly discuss extensions of the model to genomic-scale data sets and the case of a latent phylogenetic tree. © 2022 ICLR 2022 - 10th International Conference on Learning Representationss. All rights reserved.

关键词： Genome

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Biological LC/MS preprocessing and analysis with jABC, jETI and xcms

Biological LC/MS preprocessing and analysis with jABC, jETI ...

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2nd International Symposium on Leveraging Applications of Formal Methods, Verification and Validation, ISoLA 2006

作者： Kubczak, Christian Margaria, Tiziana Fritsch, Arno Steffen, Bernhard Department of Software Engineering University of Dortmund Germany Department of Service and Software Engineering University of Potsdam Germany Department of Mathematical Statistics and Biometric Applications University of Dortmund Germany Department of Programming Systems University of Dortmund Germany

ISBN: (纸本)0769530710

LC/MS is a successful analysis technique for the statistical analysis used in several branches of biology. It requires an intense screening and combination of the raw data, which is usually done with programs and libraries invoked by scripts in the domain-specific statistics language S or R. We show here how to model and implement this complex workflow in a service-oriented fashion, using the jABC service definition environment and jETI for remote service integration and execution. © 2007 IEEE.

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Sequential Monitoring of Conditional Randomization Tests: Generalized Biased Coin Designs

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Sequential Analysis 2008年第3期27卷 234-253页

作者： Yanqiong Zhang Yanqiong.Zhang@*** Rosenberger, William F. Department of Biostatistics and Programming Sanofi-Aventis Malvern Pennsylvania USA Department of Statistics George Mason University Fairfax Virginia USA

For the generalized biased coin class of randomization procedures, Smythe (1988) proved asymptotic normality of the conditional linear rank test. Clinical trialists often undertake interim analysis to determine whether to stop the trial early for a substantial treatment effect. In this article, we will set up one interim analysis using a conditional randomization test. The joint asymptotic distribution of the interim test statistic and the final test statistic will be explored. We also define the concept of conditional information under a randomization model. [ABSTRACT FROM AUTHOR]

关键词： Conditional information fraction Joint asymptotic normality Linear rank test Spending functions

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Sequential monitoring of conditional randomization tests: Generalized biased coin designs

Sequential monitoring of conditional randomization tests: Ge...

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作者： Zhang, Yanqiong Rosenberger, William F. Department of Biostatistics and Programming Sanofi-Aventis Malvern PA United States Department of Statistics George Mason University Fairfax VA United States Sanofi-Aventis 9 Great Valley Parkway Malvern PA 19355 United States

关键词： statistics

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PREDICTION OF HEART DISEASE USING CLASSIFICATION AND FEATURE SELECTION METHODS 17

PREDICTION OF HEART DISEASE USING CLASSIFICATION AND FEATURE...

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17th International Symposium on Operational Research in Slovenia, SOR 2023

作者： Akarçay Pervin, Özlem Yapici Pehlivan, Nimet Weber, Gerhard Wilhelm KTO Karatay University Department of Computer Programming Konya Turkey Selçuk University Department of Statistics Konya Turkey Poznan University of Technology Poznan Poland Middle East Technical University Institute of Applied Mathematics Ankara Turkey

ISBN: (纸本)9789616165617

Heart disease, known as cardiovascular disease has been one of the main causes of death worldwide in recent years. It is affected by various risk factors such as high blood pressure, high cholesterol, diabetes, smoking, obesity, physical inactivity, etc. In this study, Cleveland database of UCI repository of heart disease patients is used. The dataset includes 303 instances and 76 variables which of 14 variables are considered. At first, three different feature selection techniques, namely LASSO, Elastic Net and Adaptive LASSO under specific tuning parameters determined by cross-validation, are applied to reduce the number of independent variables used for the prediction. Then, five different classification techniques, K-nearest neighbor, Support Vector Machine, Decision Tree, Random Forest, and Logistic Regression are performed to predict heart disease. Performances of these methods are measured by different metrics, such as accuracy, sensitivity, specificity, and Matthew correlation coefficient. © Proceedings of the 17th International Symposium on Operational Research in Slovenia, SOR 2023. All rights reserved.

关键词： Decision trees

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