The Experts below are selected from a list of 171720 Experts worldwide ranked by ideXlab platform

Antonio Valdovinos - One of the best experts on this subject based on the ideXlab platform.

  • proposal and evaluation of ble Discovery Process based on new features of bluetooth 5 0
    Sensors, 2017
    Co-Authors: Angela Hernandezsolana, David Perezdiazdecerio, Antonio Valdovinos, Jose Luis Valenzuela
    Abstract:

    The device Discovery Process is one of the most crucial aspects in real deployments of sensor networks. Recently, several works have analyzed the topic of Bluetooth Low Energy (BLE) device Discovery through analytical or simulation models limited to version 4.x. Non-connectable and non-scannable undirected advertising has been shown to be a reliable alternative for discovering a high number of devices in a relatively short time period. However, new features of Bluetooth 5.0 allow us to define a variant on the device Discovery Process, based on BLE scannable undirected advertising events, which results in higher discovering capacities and also lower power consumption. In order to characterize this new device Discovery Process, we experimentally model the real device behavior of BLE scannable undirected advertising events. Non-detection packet probability, Discovery probability, and Discovery latency for a varying number of devices and parameters are compared by simulations and experimental measurements. We demonstrate that our proposal outperforms previous works, diminishing the Discovery time and increasing the potential user device density. A mathematical model is also developed in order to easily obtain a measure of the potential capacity in high density scenarios.

  • analytical and experimental performance evaluation of ble neighbor Discovery Process including non idealities of real chipsets
    Sensors, 2017
    Co-Authors: David Perez Diaz De Cerio, Jose Luis Valenzuela, Angela Hernandez, Antonio Valdovinos
    Abstract:

    The purpose of this paper is to evaluate from a real perspective the performance of Bluetooth Low Energy (BLE) as a technology that enables fast and reliable Discovery of a large number of users/devices in a short period of time. The BLE standard specifies a wide range of configurable parameter values that determine the Discovery Process and need to be set according to the particular application requirements. Many previous works have been addressed to investigate the Discovery Process through analytical and simulation models, according to the ideal specification of the standard. However, measurements show that additional scanning gaps appear in the scanning Process, which reduce the Discovery capabilities. These gaps have been identified in all of the analyzed devices and respond to both regular patterns and variable events associated with the decoding Process. We have demonstrated that these non-idealities, which are not taken into account in other studies, have a severe impact on the Discovery Process performance. Extensive performance evaluation for a varying number of devices and feasible parameter combinations has been done by comparing simulations and experimental measurements. This work also includes a simple mathematical model that closely matches both the standard implementation and the different chipset peculiarities for any possible parameter value specified in the standard and for any number of simultaneous advertising devices under scanner coverage.

Y K Malaiya - One of the best experts on this subject based on the ideXlab platform.

  • modeling vulnerability Discovery Process in apache and iis http servers
    Computers & Security, 2011
    Co-Authors: Sungwhan Woo, Omar H Alhazmi, Hyunchul Joh, Y K Malaiya
    Abstract:

    Vulnerability Discovery models allow prediction of the number of vulnerabilities that are likely to be discovered in the future. Hence, they allow the vendors and the end users to manage risk by optimizing resource allocation. Most vulnerability Discovery models proposed use the time as an independent variable. Effort-based modeling has also been proposed, which requires the use of market share data. Here, the feasibility of characterizing the vulnerability Discovery Process in the two major HTTP servers, Apache and IIS, is quantitatively examined using both time and effort-based vulnerability Discovery models, using data spanning more than a decade. The data used incorporates the effect of software evolution for both servers. In addition to aggregate vulnerabilities, different groups of vulnerabilities classified using both the error types and severity levels are also examined. Results show that the selected vulnerability Discovery models of both types can fit the data of the two HTTP servers very well. Results also suggest that separate modeling for an individual class of vulnerabilities can be done. In addition to the model fitting, predictive capabilities of the two models are also examined. The results demonstrate the applicability of quantitative methods to widely-used products, which have undergone evolution.

  • application of vulnerability Discovery models to major operating systems
    IEEE Transactions on Reliability, 2008
    Co-Authors: Omar H Alhazmi, Y K Malaiya
    Abstract:

    A number of security vulnerabilities have been reported in the Windows, and Linux operating systems. Both the developers, and users of operating systems have to utilize significant resources to evaluate, and mitigate the risk posed by these vulnerabilities. Vulnerabilities are discovered throughout the life of a software system by both the developers, and external testers. Vulnerability Discovery models are needed that describe the vulnerability Discovery Process for determining readiness for release, future resource allocation for patch development, and evaluating the risk of vulnerability exploitation. Here, we analytically describe six models that have been recently proposed, and evaluate those using actual data for four major operating systems. The applicability of the proposed models, and the significance of the parameters involved are examined. The results show that some of the models tend to capture the Discovery Process better than others.

  • vulnerability Discovery in multi version software systems
    High-Assurance Systems Engineering, 2007
    Co-Authors: Y K Malaiya
    Abstract:

    The vulnerability Discovery Process for a program describes the rate at which the security vulnerabilities are discovered. Being able to predict the vulnerability Discovery Process allows developers to adequately plan for resource allocation needed to develop patches for them. It also enables the users to assess the security risks. Thus there is a need to develop a model of the Discovery Process that can predict the number of vulnerabilities that are likely to be discovered in a given time frame. Recent studies have produced vulnerability Discovery Process models that are suitable for a specific version of a software. However, these models may not accurately estimate the vulnerability Discovery rates for a software when we consider successive versions, hi this paper, we propose a new approach for quantitatively modeling the vulnerability Discovery Process, based on shared source code measurements among multi-version software systems. Such a modeling approach can be used for assessing security risk both before and after the release of a version. The applicability of the approach is examined using two open source software systems, viz., Apache HTTP Web server and Mysql DataBase Management System (DBMS). We have examined the relationship between shared code size and shared vulnerabilities between two successive versions. We observe that vulnerabilities continue to be discovered for an older version because part of its code is shared by the newer and more popular later version. Thus, even when the installed base of an older version has declined, vulnerabilities applicable to it are still discovered. Our results are validated using the source code and vulnerability data for two major versions of Apache HTTP Web server and two major versions of Mysql DBMS.

  • modeling the vulnerability Discovery Process
    International Symposium on Software Reliability Engineering, 2005
    Co-Authors: Omar H Alhazmi, Y K Malaiya
    Abstract:

    Security vulnerabilities in servers and operating systems are software defects that represent great risks. Both software developers and users are struggling to contain the risk posed by these vulnerabilities. The vulnerabilities are discovered by both developers and external testers throughout the life-span of a software system. A few models for the vulnerability Discovery Process have just been published recently. Such models will allow effective resource allocation for patch development and are also needed for evaluating the risk of vulnerability exploitation. Here we examine these models for the vulnerability Discovery Process. The models are examined both analytically and using actual data on vulnerabilities discovered in three widely-used systems. The applicability of the proposed models and significance of the parameters involved are discussed. The limitations of the proposed models are examined and major research challenges are identified

Laura Maccari - One of the best experts on this subject based on the ideXlab platform.

  • straightforward recursive partitioning model for discarding insoluble compounds in the drug Discovery Process
    Journal of Medicinal Chemistry, 2008
    Co-Authors: Claudia Lamanna, Marta Bellini, Alessandro Padova, G Westerberg, Laura Maccari
    Abstract:

    Poor aqueous solubility is one of the major issues in drug Discovery and development, impacting negatively on all aspects of the research and development Process. The pharmaceutical industry has realized that solubility issues need to be resolved at the Discovery stage. We here present an innovative way to address this problem via a model designed to address the simple question, “Is the compound likely to be sufficiently soluble to provide interpretable data in biological screening assays?” A recursive partitioning (RP) method was applied to a set of 3563 molecules, with in house determined aqueous solubility values. Five models were generated on the basis of a small number of descriptors affording intuitive information regarding structural features influencing solubility. The final model was based on only two descriptors: the molecular weight (MW) and the aromatic proportion (AP). This model provided satisfactory values of accuracy (81%) and precision (75%) for a test set of 1200 compounds, suggesting th...

  • straightforward recursive partitioning model for discarding insoluble compounds in the drug Discovery Process
    Journal of Medicinal Chemistry, 2008
    Co-Authors: Claudia Lamanna, Marta Bellini, Alessandro Padova, G Westerberg, Laura Maccari
    Abstract:

    Poor aqueous solubility is one of the major issues in drug Discovery and development, impacting negatively on all aspects of the research and development Process. The pharmaceutical industry has realized that solubility issues need to be resolved at the Discovery stage. We here present an innovative way to address this problem via a model designed to address the simple question, "Is the compound likely to be sufficiently soluble to provide interpretable data in biological screening assays?" A recursive partitioning (RP) method was applied to a set of 3563 molecules, with in house determined aqueous solubility values. Five models were generated on the basis of a small number of descriptors affording intuitive information regarding structural features influencing solubility. The final model was based on only two descriptors: the molecular weight (MW) and the aromatic proportion (AP). This model provided satisfactory values of accuracy (81%) and precision (75%) for a test set of 1200 compounds, suggesting that the model may add value in compound selection and library design during early drug Discovery.

Dong Geun Jeong - One of the best experts on this subject based on the ideXlab platform.

  • Performance analysis of neighbor Discovery Process in bluetooth low-energy networks
    IEEE Transactions on Vehicular Technology, 2017
    Co-Authors: Wha Sook Jeon, Made Harta Dwijaksara, Dong Geun Jeong
    Abstract:

    To support various Internet of Things (IoT) applications, the Bluetooth Low Energy (BLE) standard specifies a wide range of parameter values for the neighbor Discovery Process (NDP). The parameter values used during neighbor Discovery directly affect the performance of the NDP. Therefore, an optimal parameter setting is essential to achieve the best tradeoff between Discovery latency and energy consumption. An analytical model can offer a beneficial guideline for such a parameter selection. In this paper, we propose a general model for analyzing the performance of NDP in BLE networks. In the model, the operations of the scanner and the advertiser, which are two main components of NDP, are expressed on the discrete-time axis. Based on the Chinese Remainder Theorem (CRT), the Discovery latency and energy consumption of advertiser are derived. The numerical results from our model are almost the same as the simulation results, for any parameter values specified by the standard. When considering that BLE is one of candidate communication technologies for IoT, the proposed model is expected to be very useful in setting the default or initial values of NDP parameters for various IoT applications.

Francisco Climent - One of the best experts on this subject based on the ideXlab platform.

  • cross listing price Discovery and the informativeness of the trading Process
    Journal of Financial Markets, 2006
    Co-Authors: Roberto Pascual, Bartolome Pascualfuster, Francisco Climent
    Abstract:

    Abstract This paper analyzes the price Discovery Process of securities that trade on multiple markets with trading sessions that totally or partially overlap. Building on Hasbrouck's (1995) information share approach, we introduce a methodology that distinguishes two sources of information asymmetries between markets: trade-related and trade-unrelated informative shocks. This approach determines how much of each market's relative contribution to the price Discovery Process (during the overlapping period) is attributable to its own trading activity. We provide empirical evidence on the contribution of the NYSE to the price Discovery Process of Spanish cross-listed stocks during the daily (two-hour) overlapping interval.

  • cross listing price Discovery and the informativeness of the trading Process
    Social Science Research Network, 2006
    Co-Authors: Roberto Pascual, Bartolome Pascualfuster, Francisco Climent
    Abstract:

    This paper analyzes the price Discovery Process of securities that trade at multiple markets with trading sessions that totally or partially overlap. Building on Hasbrouck (1995) information share approach, we introduce a methodology that distinguishes two sources of information asymmetries between markets: traderelated and trade-unrelated informative shocks. This approach determines how much of each market's relative contribution to the price Discovery Process during the overlapping period is attributable to its own trading activity. We provide empirical evidence on the contribution of the NYSE in the price Discovery Process of the Spanish cross-listed stocks during the daily two-hour overlapping interval.