Vulnerability Discovery Modeling for Open and Closed Source Software

2016 ◽  
Vol 7 (4) ◽  
pp. 19-38 ◽  
Author(s):  
Ruchi Sharma ◽  
Ritu Sibal ◽  
A.K. Shrivastava
Keyword(s):  
Logistic Model ◽  
Goodness Of Fit ◽  
Real Life ◽  
Optimal Resource Allocation ◽  
Security Risks ◽  
Proposed Model ◽  
Optimal Resource ◽  
Vulnerability Discovery ◽  
Closed Source ◽  
Decisive Parameter

With growing concern for security, the researchers began with the quantitative modeling of vulnerabilities termed as vulnerability discovery models (VDM). These models aim at finding the trend of vulnerability discovery with time and facilitate the developers in patch management, optimal resource allocation and assessing associated security risks. Among the existing models for vulnerability discovery, Alhazmi-Malaiya Logistic Model (AML) is considered the best fitted model on all kinds of datasets. But, each of the existing models has a predefined basic shape and can only fit datasets following their basic shapes. Thus, shape of the dataset forms the decisive parameter for model selection. In this paper, the authors have proposed a new model to capture a wide variety of datasets irrespective of their shape accounting for better goodness of fit. The proposed model has been evaluated on three real life datasets each for open and closed source software and the models are ranked based on their suitability to discover vulnerabilities using normalized criteria distance (NCD) technique.

Grid Resource Discovery Strategies Base on Multi-Economic Agent

2010 ◽  
Vol 29-32 ◽  
pp. 1100-1108
Author(s):  
Jun Xie ◽  
Ji Guang Li
Keyword(s):  
Resource Allocation ◽  
Resource Discovery ◽  
Optimal Resource Allocation ◽  
Grid Resource ◽  
Trading Agents ◽  
Proposed Model ◽  
Finite Set ◽  
Optimal Resource ◽  
Resource Allocation Strategy ◽  
Grid Resource Discovery

The paper presents a market oriented resource allocation strategy for grid resource. The proposed model uses the utility functions for calculating the utility of a resource allocation. This paper is target to solve above issues by using utility-based optimization scheme. We firstly point out the factors that influence the resources’ prices; then make out the trading flow for resource consumer agents and provider agents. By doing these, the two trading agents can decide their price due to the dynamic changes of the Grid environment without any manmade interferences. Total user benefit of the computational grid is maximized when the equilibrium prices are obtained through the consumer’s market optimization and provider’s market optimization. The economic model is the basis of an iterative algorithm that, given a finite set of requests, is used to perform optimal resource allocation.

Adjusting a subject-specific time of event in longitudinal studies

2019 ◽  
Vol 29 (7) ◽  
pp. 1787-1798
Author(s):  
Hyunkeun Ryan Cho ◽  
Seonjin Kim ◽  
Myung Hee Lee
Keyword(s):  
Goodness Of Fit ◽  
Real Life ◽  
Model Parameters ◽  
Specific Time ◽  
Goodness Of Fit Test ◽  
Proposed Model ◽  
Subject Specific ◽  
The Subject ◽  
The Mean ◽  
Over Time

Biomedical studies often involve an event that occurs to individuals at different times and has a significant influence on individual trajectories of response variables over time. We propose a statistical model to capture the mean trajectory alteration caused by not only the occurrence of the event but also the subject-specific time of the event. The proposed model provides a post-event mean trajectory smoothly connected with the pre-event mean trajectory by allowing the model parameters associated with the post-event mean trajectory to vary over time of the event. A goodness-of-fit test is considered to investigate how well the proposed model is fit to the data. Hypothesis tests are also developed to assess the influence of the subject-specific time of event on the mean trajectory. Theoretical and simulation studies confirm that the proposed tests choose the correctly specified model consistently and examine the effect of the subject-specific time of event successfully. The proposed model and tests are also illustrated by the analysis of two real-life data from a biomarker study for HIV patients along with their own time of treatment initiation and a body fatness study in girls with different age of menarche.

The shifted Gompertz-G family of distributions: Properties and applications

2021 ◽  
Vol 71 (5) ◽  
pp. 1291-1308
Author(s):  
Joseph Thomas Eghwerido ◽  
Friday Ikechukwu Agu
Keyword(s):  
Goodness Of Fit ◽  
Maximum Likelihood Method ◽  
Real Life ◽  
Likelihood Method ◽  
Test Statistic ◽  
Data Set ◽  
Monte Carlo Simulation Study ◽  
Proposed Model ◽  
Left And Right ◽  
Mean Variance

Abstract This article proposes a class of generator for classical statistical distribution called the shifted Gompertz-G (SHIGO-G) distribution for generating new continuous distributions. Special models of the proposed model were examined together with some of its statistical properties in closed form which makes it tractable for censored data. Its major properties include heavy tail, approximately symmetric, left and right skewed with a combination of exponential and a reverted Gumbel distributions called the Gompertz. The bivariate SHIGO-G is introduced. The parameters estimate of the proposed model was obtained by maximum likelihood method. A Monte Carlo simulation study was employed to investigate the performance of the estimators of the proposed model mean, variance, bias and mean square error. A two real life illustration was used to examine the empirical goodness-of-fit of the test statistic of the proposed model. The results of the real life applications show that the SHIGO-G model provides a better fit for the data set used.

Applications of Truncated Cauchy Power Log-Logistic Model to Physical and Biomedical Data

2020 ◽  
Vol 12 (1) ◽  
pp. 25-33
Author(s):  
Majdah M. Badr
Keyword(s):  
Logistic Model ◽  
Maximum Likelihood Method ◽  
Numerical Study ◽  
Real Life ◽  
Quantile Function ◽  
Moment Generating Function ◽  
Likelihood Method ◽  
Biomedical Data ◽  
Proposed Model ◽  
Lifetime Model

In this article, we introduce a new three-parameter lifetime model, which is called truncated Cauchy power Log-Logistic (TCPLL) model. The TCPLL model has many applications in different sciences, such as physics and medicine, and we show that in the application section. We used two real-life datasets related to physics and medicine to show the flexibility of the TCPLL model. The TCPLL distribution is more flexible than some well-known models. The TCPLL parameters are estimated using maximum likelihood method for estimation. The numerical study is displayed to show the effectiveness of the estimates. At the end, we calculated some important properties like, quantile function, moments, order statistics and moment generating function of the proposed model.

scholarly journals Generalized Lindley-Quasi Xgamma distribution

2021 ◽  
Vol 17 (1) ◽  
pp. 5-30
Author(s):  
S. A. Wani ◽  
S. Shafi
Keyword(s):  
Maximum Likelihood ◽  
Goodness Of Fit ◽  
Real Life ◽  
Estimation Method ◽  
Maximum Likelihood Estimates ◽  
Ratio Test ◽  
Life Data ◽  
Weight Parameter ◽  
Real Life Data ◽  
Proposed Model

Abstract We obtained a new generalization of Lindley-Quasi Xgamma distribution by adding weight parameter to it through weighting technique and have shown the flexibility of proposed model. Expression for reliability measures, order statistics, Bonferroni curves & indices, Renyi entropy along with some other important properties are derived. Maximum likelihood estimation method is put to use for estimation of unknown parameters of proposed model. Simulation study for checking the performance of maximum likelihood estimates and for model comparison is carried out. Proposed model and its related models are fitted to real life data sets and goodness of fit measure Kolmogorov statistic & p-value, loss of information criteria’s AIC, BIC, AICC & HQIC are computed through R software to check the applicability of proposed model in real life. The significance of weight parameter is also tested by using likelihood ratio test for both randomly generated data as well as real life data.

Bi-Criterion Problem to Determine Optimal Vulnerability Discovery and Patching Time

2018 ◽  
Vol 25 (01) ◽  
pp. 1850002 ◽  
Author(s):  
Swati Narang ◽  
P. K. Kapur ◽  
D. Damodaran ◽  
A. K. Shrivastava
Keyword(s):  
Software Security ◽  
Real Life ◽  
Data Set ◽  
Security Breach ◽  
Software Vulnerabilities ◽  
Real Life Data ◽  
Proposed Model ◽  
Software Firms ◽  
Vulnerability Discovery ◽  
Discovery Time

In the last decade, we have seen enormous growth in software security related problems. This is due to the presence of bad guys who keep eye on the software vulnerabilities and create the security breach. Because of which software firms face huge loss. The problems of the software firms is two folded. One is to decide the optimal discovery time of the software vulnerability and another one is to determine the optimal patching time of those discovered vulnerability. Optimal discovery time of vulnerability is necessary as not disclosing the vulnerability on time may cause serious loss in the coming future. On the other hand, after discovering the vulnerabilities, it is more important to fix them too. Fixing of vulnerabilities is done by patching. But when to patch the vulnerabilities is also a great concern for the software firms. As delay in patch may cause more breaches in security and disadoption of the software and early patching early may reduce the risk but bad patching may increase the risk of security breach even after remedial patch release. In the current work, we have proposed a bi-criterion framework to minimizing cost and risk together under risk and budgetary constraints to determine the optimal vulnerability discovery and patching time. The proposed model is validated using real life data set.

Risk-Based Robust Bidding Strategies for EVs’ Aggregators in Day-ahead Markets with Uncertainty

2020 ◽  
Author(s):  
Ahmed Abdelmoaty ◽  
Wessam Mesbah ◽  
Mohammad A. M. Abdel-Aal ◽  
Ali T. Alawami
Keyword(s):  
Robust Optimization ◽  
Electricity Market ◽  
Real Life ◽  
Optimization Approach ◽  
Case Scenario ◽  
Worst Case ◽  
Bidding Strategies ◽  
Wind Generators ◽  
Proposed Model ◽  
Optimal Bidding

In the recent electricity market framework, the profit of the generation companies depends on the decision of the operator on the schedule of its units, the energy price, and the optimal bidding strategies. Due to the expanded integration of uncertain renewable generators which is highly intermittent such as wind plants, the coordination with other facilities to mitigate the risks of imbalances is mandatory. Accordingly, coordination of wind generators with the evolutionary Electric Vehicles (EVs) is expected to boost the performance of the grid. In this paper, we propose a robust optimization approach for the coordination between the wind-thermal generators and the EVs in a virtual<br>power plant (VPP) environment. The objective of maximizing the profit of the VPP Operator (VPPO) is studied. The optimal bidding strategy of the VPPO in the day-ahead market under uncertainties of wind power, energy<br>prices, imbalance prices, and demand is obtained for the worst case scenario. A case study is conducted to assess the e?effectiveness of the proposed model in terms of the VPPO's profit. A comparison between the proposed model and the scenario-based optimization was introduced. Our results confirmed that, although the conservative behavior of the worst-case robust optimization model, it helps the decision maker from the fluctuations of the uncertain parameters involved in the production and bidding processes. In addition, robust optimization is a more tractable problem and does not suffer from<br>the high computation burden associated with scenario-based stochastic programming. This makes it more practical for real-life scenarios.<br>

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