The Experts below are selected from a list of 14418 Experts worldwide ranked by ideXlab platform

Ruzica Piskac - One of the best experts on this subject based on the ideXlab platform.

  • synthesizing Configuration File specifications with association rule learning
    Conference on Object-Oriented Programming Systems Languages and Applications, 2017
    Co-Authors: Mark Santolucito, Ennan Zhai, Rahul Dhodapkar, Aaron Shim, Ruzica Piskac
    Abstract:

    System failures resulting from Configuration errors are one of the major reasons for the compromised reliability of today's software systems. Although many techniques have been proposed for Configuration error detection, these approaches can generally only be applied after an error has occurred. Proactively verifying Configuration Files is a challenging problem, because 1) software Configurations are typically written in poorly structured and untyped "languages", and 2) specifying rules for Configuration verification is challenging in practice. This paper presents VeriConf, a verification framework for general software Configurations. Our framework works as follows: in the pre-processing stage, we first automatically derive a specification. Once we have a specification, we check if a given Configuration File adheres to that specification. The process of learning a specification works through three steps. First, VeriConf parses a training set of Configuration Files (not necessarily all correct) into a well-structured and probabilistically-typed intermediate representation. Second, based on the association rule learning algorithm, VeriConf learns rules from these intermediate representations. These rules establish relationships between the keywords appearing in the Files. Finally, VeriConf employs rule graph analysis to refine the resulting rules. VeriConf is capable of detecting various Configuration errors, including ordering errors, integer correlation errors, type errors, and missing entry errors. We evaluated VeriConf by verifying public Configuration Files on Github, and we show that VeriConf can detect known Configuration errors in these Files.

  • synthesizing Configuration File specifications with association rule learning
    Conference on Object-Oriented Programming Systems Languages and Applications, 2017
    Co-Authors: Mark Santolucito, Ennan Zhai, Rahul Dhodapkar, Aaron Shim, Ruzica Piskac
    Abstract:

    System failures resulting from Configuration errors are one of the major reasons for the compromised reliability of today's software systems. Although many techniques have been proposed for Configuration error detection, these approaches can generally only be applied after an error has occurred. Proactively verifying Configuration Files is a challenging problem, because 1) software Configurations are typically written in poorly structured and untyped “languages”, and 2) specifying rules for Configuration verification is challenging in practice. This paper presents ConfigV, a verification framework for general software Configurations. Our framework works as follows: in the pre-processing stage, we first automatically derive a specification. Once we have a specification, we check if a given Configuration File adheres to that specification. The process of learning a specification works through three steps. First, ConfigV parses a training set of Configuration Files (not necessarily all correct) into a well-structured and probabilistically-typed intermediate representation. Second, based on the association rule learning algorithm, ConfigV learns rules from these intermediate representations. These rules establish relationships between the keywords appearing in the Files. Finally, ConfigV employs rule graph analysis to refine the resulting rules. ConfigV is capable of detecting various Configuration errors, including ordering errors, integer correlation errors, type errors, and missing entry errors. We evaluated ConfigV by verifying public Configuration Files on GitHub, and we show that ConfigV can detect known Configuration errors in these Files.

Xiuchen Jiang - One of the best experts on this subject based on the ideXlab platform.

  • research on electrical power system with information modeling and scl description of ied for transformer condition monitoring based on iec61850
    Advanced Materials Research, 2013
    Co-Authors: Yun Tao Sun, Xin Jian Tang, Xiuchen Jiang
    Abstract:

    A transformer is a critical device in an electrical power system substation. Monitoring its condition, including insulation material statuses, is imperative. This article discusses the information modeling techniques of IEC61850 and modeling steps of condition-monitoring IED, analyzes the reporting service, focuses on the characteristics of transformer condition monitoring, rationally develops and makes use of logical nodes and data in the IEC61850 standard, conducts modeling for the actual intelligent electronic device (IED) of transformer condition monitoring and describes the model with substation Configuration description language (SCL). The Configuration File of this model has passed the test done with standard syntax (Schema mode).

  • Information Modeling and SCL Description of IED for Transformer Condition Monitoring Based on IEC61850
    Power and Energy Engineering Conference (APPEEC), 2012 Asia-Pacific, 2012
    Co-Authors: Jian Xu, Hangjie Wu, Jianxin Yao, Yadong Liu, Gehao Sheng, Ming Lv, Xiuchen Jiang
    Abstract:

    Transformer is an important substation device of power system and monitoring of its condition is imperative. IEC61850 standard as an international standard of substation communication networks and systems is aimed at harmonizing the current stand-alone substation automation systems and realizing the interoperation of intelligent electronic devices (IED) made by different manufacturers. This article discusses the information modeling techniques of IEC61850 and modeling steps of condition-monitoring IED, analyzes the reporting service, focuses on the characteristics of transformer condition monitoring, rationally develops and makes use of logical nodes and data in the IEC61850 standard, conducts modeling for the actual intelligent electronic device (IED) of transformer condition monitoring and describes the model with substation Configuration description language (SCL). The Configuration File of this model has passed the test done with standard syntax (Schema mode).

Alessandro Provetti - One of the best experts on this subject based on the ideXlab platform.

  • policy based parametric firewall Configuration a real case application
    IEEE International Workshop on Policies for Distributed Systems and Networks, 2007
    Co-Authors: M. Marchi, R. Penzo, Alessandro Provetti
    Abstract:

    We describe a simple policy language for setting up and running firewalls (FW). The language allows to describe sophisticated policies for controlling network connections. Composition is done at set-up time, when a parser, starting from a given policy, generates the relative Configuration File for one or more firewalls operating the industry-standard Linux Iptables kernel extension. The policy captures the essence of the desired requirements and constrains upon connections between zones. The language has been designed and it is currently on testing in the context of a large intra/extranet with more than 10,000 assigned IP addresses.

Mark Santolucito - One of the best experts on this subject based on the ideXlab platform.

  • synthesizing Configuration File specifications with association rule learning
    Conference on Object-Oriented Programming Systems Languages and Applications, 2017
    Co-Authors: Mark Santolucito, Ennan Zhai, Rahul Dhodapkar, Aaron Shim, Ruzica Piskac
    Abstract:

    System failures resulting from Configuration errors are one of the major reasons for the compromised reliability of today's software systems. Although many techniques have been proposed for Configuration error detection, these approaches can generally only be applied after an error has occurred. Proactively verifying Configuration Files is a challenging problem, because 1) software Configurations are typically written in poorly structured and untyped "languages", and 2) specifying rules for Configuration verification is challenging in practice. This paper presents VeriConf, a verification framework for general software Configurations. Our framework works as follows: in the pre-processing stage, we first automatically derive a specification. Once we have a specification, we check if a given Configuration File adheres to that specification. The process of learning a specification works through three steps. First, VeriConf parses a training set of Configuration Files (not necessarily all correct) into a well-structured and probabilistically-typed intermediate representation. Second, based on the association rule learning algorithm, VeriConf learns rules from these intermediate representations. These rules establish relationships between the keywords appearing in the Files. Finally, VeriConf employs rule graph analysis to refine the resulting rules. VeriConf is capable of detecting various Configuration errors, including ordering errors, integer correlation errors, type errors, and missing entry errors. We evaluated VeriConf by verifying public Configuration Files on Github, and we show that VeriConf can detect known Configuration errors in these Files.

  • synthesizing Configuration File specifications with association rule learning
    Conference on Object-Oriented Programming Systems Languages and Applications, 2017
    Co-Authors: Mark Santolucito, Ennan Zhai, Rahul Dhodapkar, Aaron Shim, Ruzica Piskac
    Abstract:

    System failures resulting from Configuration errors are one of the major reasons for the compromised reliability of today's software systems. Although many techniques have been proposed for Configuration error detection, these approaches can generally only be applied after an error has occurred. Proactively verifying Configuration Files is a challenging problem, because 1) software Configurations are typically written in poorly structured and untyped “languages”, and 2) specifying rules for Configuration verification is challenging in practice. This paper presents ConfigV, a verification framework for general software Configurations. Our framework works as follows: in the pre-processing stage, we first automatically derive a specification. Once we have a specification, we check if a given Configuration File adheres to that specification. The process of learning a specification works through three steps. First, ConfigV parses a training set of Configuration Files (not necessarily all correct) into a well-structured and probabilistically-typed intermediate representation. Second, based on the association rule learning algorithm, ConfigV learns rules from these intermediate representations. These rules establish relationships between the keywords appearing in the Files. Finally, ConfigV employs rule graph analysis to refine the resulting rules. ConfigV is capable of detecting various Configuration errors, including ordering errors, integer correlation errors, type errors, and missing entry errors. We evaluated ConfigV by verifying public Configuration Files on GitHub, and we show that ConfigV can detect known Configuration errors in these Files.

Michaël Rusinowitch - One of the best experts on this subject based on the ideXlab platform.

  • Detection of firewall Configuration errors with updatable tree
    International Journal of Information Security, 2016
    Co-Authors: Tarek Abbes, Adel Bouhoula, Michaël Rusinowitch
    Abstract:

    The fundamental goals of security policy are to allow uninterrupted access to the network resources for authenticated users and to deny access to unauthenticated users. For this purpose, firewalls are frequently deployed in every size network. However, bad Configurations may cause serious security breaches and network vulnerabilities. In particular, conflicted filtering rules lead to block legitimate traffic and to accept unwanted packets. This fact troubles administrators who have to insert and delete filtering rules in a huge Configuration File. We propose in this paper a quick method for managing a firewall Configuration File. We represent the set of filtering rules by a firewall anomaly tree (FAT). Then, an administrator can update the FAT by inserting and deleting some filtering rules. The FAT modification automatically reveals emerged anomalies and helps the administrator to find the adequate position for a new added filtering rule. All the algorithms presented in the paper have been implemented, and computer experiments show the usefulness of updating the FAT data structure in order to quickly detect anomalies when dealing with a huge firewall Configuration File.