The Experts below are selected from a list of 159 Experts worldwide ranked by ideXlab platform

Angelos D. Keromytis - One of the best experts on this subject based on the ideXlab platform.

  • A Stack Memory Abstraction and Symbolic Analysis Framework for Executables
    ACM Transactions on Software Engineering and Methodology, 2016
    Co-Authors: Kapil Anand, Khaled Elwazeer, Aparna Kotha, Matthew Smithson, Rajeev Barua, Angelos D. Keromytis
    Abstract:

    This article makes three contributions regarding reverse-engineering of executables. First, techniques are presented for recovering a precise and correct Stack-Memory model in executables while addressing executable-specific challenges such as indirect control transfers. Next, the enhanced Memory model is employed to define a novel symbolic analysis framework for executables that can perform the same types of program analyses as source-level tools. Third, a demand-driven framework is presented to enhance the scalability of the symbolic analysis framework. Existing symbolic analysis frameworks for executables fail to simultaneously maintain the properties of correct representation, a precise Stack-Memory model, and scalability. Furthermore, they ignore Memory-allocated variables when defining symbolic analysis mechanisms. Our methods do not use symbolic, relocation or debug information, which are usually absent in deployed binaries. We describe our framework, highlighting the novel intellectual contributions of our approach and demonstrating its efficacy and robustness. Our techniques improve the precision of existing Stack-Memory models by 25p, enhance scalability of our basic symbolic analysis mechanism by 10×, and successfully uncovers five previously undiscovered information-flow vulnerabilities in several widely used programs.

  • an accurate Stack Memory abstraction and symbolic analysis framework for executables
    International Conference on Software Maintenance, 2013
    Co-Authors: Kapil Anand, Khaled Elwazeer, Aparna Kotha, Matthew Smithson, Rajeev Barua, Angelos D. Keromytis
    Abstract:

    This paper makes two contributions regarding reverse engineering of executables. First, techniques are presented for recovering a precise and correct Stack Memory model in executables in presence of executable-specific artifacts such as indirect control transfers. Next, the enhanced Memory model is employed to define a novel symbolic analysis framework for executables that can perform the same types of program analysis as source-level tools. Frameworks hitherto fail to simultaneously maintain the properties of correct representation and precise Memory model and ignore Memory-allocated variables while defining symbolic analysis mechanisms. Our methods do not use symbolic, relocation, or debug information, which are usually absent in deployed binaries. We describe our framework, highlighting the novel intellectual contributions of our approach, and demonstrate its efficacy and robustness by applying it to various traditional analyses, including identifying information flow vulnerabilities in five real-world programs.

  • ICSM - An Accurate Stack Memory Abstraction and Symbolic Analysis Framework for Executables
    2013 IEEE International Conference on Software Maintenance, 2013
    Co-Authors: Kapil Anand, Khaled Elwazeer, Aparna Kotha, Matthew Smithson, Rajeev Barua, Angelos D. Keromytis
    Abstract:

    This paper makes two contributions regarding reverse engineering of executables. First, techniques are presented for recovering a precise and correct Stack Memory model in executables in presence of executable-specific artifacts such as indirect control transfers. Next, the enhanced Memory model is employed to define a novel symbolic analysis framework for executables that can perform the same types of program analysis as source-level tools. Frameworks hitherto fail to simultaneously maintain the properties of correct representation and precise Memory model and ignore Memory-allocated variables while defining symbolic analysis mechanisms. Our methods do not use symbolic, relocation, or debug information, which are usually absent in deployed binaries. We describe our framework, highlighting the novel intellectual contributions of our approach, and demonstrate its efficacy and robustness by applying it to various traditional analyses, including identifying information flow vulnerabilities in five real-world programs.

Kapil Anand - One of the best experts on this subject based on the ideXlab platform.

  • A Stack Memory Abstraction and Symbolic Analysis Framework for Executables
    ACM Transactions on Software Engineering and Methodology, 2016
    Co-Authors: Kapil Anand, Khaled Elwazeer, Aparna Kotha, Matthew Smithson, Rajeev Barua, Angelos D. Keromytis
    Abstract:

    This article makes three contributions regarding reverse-engineering of executables. First, techniques are presented for recovering a precise and correct Stack-Memory model in executables while addressing executable-specific challenges such as indirect control transfers. Next, the enhanced Memory model is employed to define a novel symbolic analysis framework for executables that can perform the same types of program analyses as source-level tools. Third, a demand-driven framework is presented to enhance the scalability of the symbolic analysis framework. Existing symbolic analysis frameworks for executables fail to simultaneously maintain the properties of correct representation, a precise Stack-Memory model, and scalability. Furthermore, they ignore Memory-allocated variables when defining symbolic analysis mechanisms. Our methods do not use symbolic, relocation or debug information, which are usually absent in deployed binaries. We describe our framework, highlighting the novel intellectual contributions of our approach and demonstrating its efficacy and robustness. Our techniques improve the precision of existing Stack-Memory models by 25p, enhance scalability of our basic symbolic analysis mechanism by 10×, and successfully uncovers five previously undiscovered information-flow vulnerabilities in several widely used programs.

  • an accurate Stack Memory abstraction and symbolic analysis framework for executables
    International Conference on Software Maintenance, 2013
    Co-Authors: Kapil Anand, Khaled Elwazeer, Aparna Kotha, Matthew Smithson, Rajeev Barua, Angelos D. Keromytis
    Abstract:

    This paper makes two contributions regarding reverse engineering of executables. First, techniques are presented for recovering a precise and correct Stack Memory model in executables in presence of executable-specific artifacts such as indirect control transfers. Next, the enhanced Memory model is employed to define a novel symbolic analysis framework for executables that can perform the same types of program analysis as source-level tools. Frameworks hitherto fail to simultaneously maintain the properties of correct representation and precise Memory model and ignore Memory-allocated variables while defining symbolic analysis mechanisms. Our methods do not use symbolic, relocation, or debug information, which are usually absent in deployed binaries. We describe our framework, highlighting the novel intellectual contributions of our approach, and demonstrate its efficacy and robustness by applying it to various traditional analyses, including identifying information flow vulnerabilities in five real-world programs.

  • ICSM - An Accurate Stack Memory Abstraction and Symbolic Analysis Framework for Executables
    2013 IEEE International Conference on Software Maintenance, 2013
    Co-Authors: Kapil Anand, Khaled Elwazeer, Aparna Kotha, Matthew Smithson, Rajeev Barua, Angelos D. Keromytis
    Abstract:

    This paper makes two contributions regarding reverse engineering of executables. First, techniques are presented for recovering a precise and correct Stack Memory model in executables in presence of executable-specific artifacts such as indirect control transfers. Next, the enhanced Memory model is employed to define a novel symbolic analysis framework for executables that can perform the same types of program analysis as source-level tools. Frameworks hitherto fail to simultaneously maintain the properties of correct representation and precise Memory model and ignore Memory-allocated variables while defining symbolic analysis mechanisms. Our methods do not use symbolic, relocation, or debug information, which are usually absent in deployed binaries. We describe our framework, highlighting the novel intellectual contributions of our approach, and demonstrate its efficacy and robustness by applying it to various traditional analyses, including identifying information flow vulnerabilities in five real-world programs.

Rajeev Barua - One of the best experts on this subject based on the ideXlab platform.

  • A Stack Memory Abstraction and Symbolic Analysis Framework for Executables
    ACM Transactions on Software Engineering and Methodology, 2016
    Co-Authors: Kapil Anand, Khaled Elwazeer, Aparna Kotha, Matthew Smithson, Rajeev Barua, Angelos D. Keromytis
    Abstract:

    This article makes three contributions regarding reverse-engineering of executables. First, techniques are presented for recovering a precise and correct Stack-Memory model in executables while addressing executable-specific challenges such as indirect control transfers. Next, the enhanced Memory model is employed to define a novel symbolic analysis framework for executables that can perform the same types of program analyses as source-level tools. Third, a demand-driven framework is presented to enhance the scalability of the symbolic analysis framework. Existing symbolic analysis frameworks for executables fail to simultaneously maintain the properties of correct representation, a precise Stack-Memory model, and scalability. Furthermore, they ignore Memory-allocated variables when defining symbolic analysis mechanisms. Our methods do not use symbolic, relocation or debug information, which are usually absent in deployed binaries. We describe our framework, highlighting the novel intellectual contributions of our approach and demonstrating its efficacy and robustness. Our techniques improve the precision of existing Stack-Memory models by 25p, enhance scalability of our basic symbolic analysis mechanism by 10×, and successfully uncovers five previously undiscovered information-flow vulnerabilities in several widely used programs.

  • an accurate Stack Memory abstraction and symbolic analysis framework for executables
    International Conference on Software Maintenance, 2013
    Co-Authors: Kapil Anand, Khaled Elwazeer, Aparna Kotha, Matthew Smithson, Rajeev Barua, Angelos D. Keromytis
    Abstract:

    This paper makes two contributions regarding reverse engineering of executables. First, techniques are presented for recovering a precise and correct Stack Memory model in executables in presence of executable-specific artifacts such as indirect control transfers. Next, the enhanced Memory model is employed to define a novel symbolic analysis framework for executables that can perform the same types of program analysis as source-level tools. Frameworks hitherto fail to simultaneously maintain the properties of correct representation and precise Memory model and ignore Memory-allocated variables while defining symbolic analysis mechanisms. Our methods do not use symbolic, relocation, or debug information, which are usually absent in deployed binaries. We describe our framework, highlighting the novel intellectual contributions of our approach, and demonstrate its efficacy and robustness by applying it to various traditional analyses, including identifying information flow vulnerabilities in five real-world programs.

  • ICSM - An Accurate Stack Memory Abstraction and Symbolic Analysis Framework for Executables
    2013 IEEE International Conference on Software Maintenance, 2013
    Co-Authors: Kapil Anand, Khaled Elwazeer, Aparna Kotha, Matthew Smithson, Rajeev Barua, Angelos D. Keromytis
    Abstract:

    This paper makes two contributions regarding reverse engineering of executables. First, techniques are presented for recovering a precise and correct Stack Memory model in executables in presence of executable-specific artifacts such as indirect control transfers. Next, the enhanced Memory model is employed to define a novel symbolic analysis framework for executables that can perform the same types of program analysis as source-level tools. Frameworks hitherto fail to simultaneously maintain the properties of correct representation and precise Memory model and ignore Memory-allocated variables while defining symbolic analysis mechanisms. Our methods do not use symbolic, relocation, or debug information, which are usually absent in deployed binaries. We describe our framework, highlighting the novel intellectual contributions of our approach, and demonstrate its efficacy and robustness by applying it to various traditional analyses, including identifying information flow vulnerabilities in five real-world programs.

Matthew Smithson - One of the best experts on this subject based on the ideXlab platform.

  • A Stack Memory Abstraction and Symbolic Analysis Framework for Executables
    ACM Transactions on Software Engineering and Methodology, 2016
    Co-Authors: Kapil Anand, Khaled Elwazeer, Aparna Kotha, Matthew Smithson, Rajeev Barua, Angelos D. Keromytis
    Abstract:

    This article makes three contributions regarding reverse-engineering of executables. First, techniques are presented for recovering a precise and correct Stack-Memory model in executables while addressing executable-specific challenges such as indirect control transfers. Next, the enhanced Memory model is employed to define a novel symbolic analysis framework for executables that can perform the same types of program analyses as source-level tools. Third, a demand-driven framework is presented to enhance the scalability of the symbolic analysis framework. Existing symbolic analysis frameworks for executables fail to simultaneously maintain the properties of correct representation, a precise Stack-Memory model, and scalability. Furthermore, they ignore Memory-allocated variables when defining symbolic analysis mechanisms. Our methods do not use symbolic, relocation or debug information, which are usually absent in deployed binaries. We describe our framework, highlighting the novel intellectual contributions of our approach and demonstrating its efficacy and robustness. Our techniques improve the precision of existing Stack-Memory models by 25p, enhance scalability of our basic symbolic analysis mechanism by 10×, and successfully uncovers five previously undiscovered information-flow vulnerabilities in several widely used programs.

  • an accurate Stack Memory abstraction and symbolic analysis framework for executables
    International Conference on Software Maintenance, 2013
    Co-Authors: Kapil Anand, Khaled Elwazeer, Aparna Kotha, Matthew Smithson, Rajeev Barua, Angelos D. Keromytis
    Abstract:

    This paper makes two contributions regarding reverse engineering of executables. First, techniques are presented for recovering a precise and correct Stack Memory model in executables in presence of executable-specific artifacts such as indirect control transfers. Next, the enhanced Memory model is employed to define a novel symbolic analysis framework for executables that can perform the same types of program analysis as source-level tools. Frameworks hitherto fail to simultaneously maintain the properties of correct representation and precise Memory model and ignore Memory-allocated variables while defining symbolic analysis mechanisms. Our methods do not use symbolic, relocation, or debug information, which are usually absent in deployed binaries. We describe our framework, highlighting the novel intellectual contributions of our approach, and demonstrate its efficacy and robustness by applying it to various traditional analyses, including identifying information flow vulnerabilities in five real-world programs.

  • ICSM - An Accurate Stack Memory Abstraction and Symbolic Analysis Framework for Executables
    2013 IEEE International Conference on Software Maintenance, 2013
    Co-Authors: Kapil Anand, Khaled Elwazeer, Aparna Kotha, Matthew Smithson, Rajeev Barua, Angelos D. Keromytis
    Abstract:

    This paper makes two contributions regarding reverse engineering of executables. First, techniques are presented for recovering a precise and correct Stack Memory model in executables in presence of executable-specific artifacts such as indirect control transfers. Next, the enhanced Memory model is employed to define a novel symbolic analysis framework for executables that can perform the same types of program analysis as source-level tools. Frameworks hitherto fail to simultaneously maintain the properties of correct representation and precise Memory model and ignore Memory-allocated variables while defining symbolic analysis mechanisms. Our methods do not use symbolic, relocation, or debug information, which are usually absent in deployed binaries. We describe our framework, highlighting the novel intellectual contributions of our approach, and demonstrate its efficacy and robustness by applying it to various traditional analyses, including identifying information flow vulnerabilities in five real-world programs.

Aparna Kotha - One of the best experts on this subject based on the ideXlab platform.

  • A Stack Memory Abstraction and Symbolic Analysis Framework for Executables
    ACM Transactions on Software Engineering and Methodology, 2016
    Co-Authors: Kapil Anand, Khaled Elwazeer, Aparna Kotha, Matthew Smithson, Rajeev Barua, Angelos D. Keromytis
    Abstract:

    This article makes three contributions regarding reverse-engineering of executables. First, techniques are presented for recovering a precise and correct Stack-Memory model in executables while addressing executable-specific challenges such as indirect control transfers. Next, the enhanced Memory model is employed to define a novel symbolic analysis framework for executables that can perform the same types of program analyses as source-level tools. Third, a demand-driven framework is presented to enhance the scalability of the symbolic analysis framework. Existing symbolic analysis frameworks for executables fail to simultaneously maintain the properties of correct representation, a precise Stack-Memory model, and scalability. Furthermore, they ignore Memory-allocated variables when defining symbolic analysis mechanisms. Our methods do not use symbolic, relocation or debug information, which are usually absent in deployed binaries. We describe our framework, highlighting the novel intellectual contributions of our approach and demonstrating its efficacy and robustness. Our techniques improve the precision of existing Stack-Memory models by 25p, enhance scalability of our basic symbolic analysis mechanism by 10×, and successfully uncovers five previously undiscovered information-flow vulnerabilities in several widely used programs.

  • an accurate Stack Memory abstraction and symbolic analysis framework for executables
    International Conference on Software Maintenance, 2013
    Co-Authors: Kapil Anand, Khaled Elwazeer, Aparna Kotha, Matthew Smithson, Rajeev Barua, Angelos D. Keromytis
    Abstract:

    This paper makes two contributions regarding reverse engineering of executables. First, techniques are presented for recovering a precise and correct Stack Memory model in executables in presence of executable-specific artifacts such as indirect control transfers. Next, the enhanced Memory model is employed to define a novel symbolic analysis framework for executables that can perform the same types of program analysis as source-level tools. Frameworks hitherto fail to simultaneously maintain the properties of correct representation and precise Memory model and ignore Memory-allocated variables while defining symbolic analysis mechanisms. Our methods do not use symbolic, relocation, or debug information, which are usually absent in deployed binaries. We describe our framework, highlighting the novel intellectual contributions of our approach, and demonstrate its efficacy and robustness by applying it to various traditional analyses, including identifying information flow vulnerabilities in five real-world programs.

  • ICSM - An Accurate Stack Memory Abstraction and Symbolic Analysis Framework for Executables
    2013 IEEE International Conference on Software Maintenance, 2013
    Co-Authors: Kapil Anand, Khaled Elwazeer, Aparna Kotha, Matthew Smithson, Rajeev Barua, Angelos D. Keromytis
    Abstract:

    This paper makes two contributions regarding reverse engineering of executables. First, techniques are presented for recovering a precise and correct Stack Memory model in executables in presence of executable-specific artifacts such as indirect control transfers. Next, the enhanced Memory model is employed to define a novel symbolic analysis framework for executables that can perform the same types of program analysis as source-level tools. Frameworks hitherto fail to simultaneously maintain the properties of correct representation and precise Memory model and ignore Memory-allocated variables while defining symbolic analysis mechanisms. Our methods do not use symbolic, relocation, or debug information, which are usually absent in deployed binaries. We describe our framework, highlighting the novel intellectual contributions of our approach, and demonstrate its efficacy and robustness by applying it to various traditional analyses, including identifying information flow vulnerabilities in five real-world programs.