The Experts below are selected from a list of 39 Experts worldwide ranked by ideXlab platform

Kebschull Udo - One of the best experts on this subject based on the ideXlab platform.

  • Intrusion Prevention and Detection in Grid Computing - The ALICE Case
    'IOP Publishing', 2017
    Co-Authors: Gomez Andres, Lara Camilo, Kebschull Udo
    Abstract:

    Grids allow users flexible on-demand usage of computing resources through remote communication networks. A remarkable example of a Grid in High Energy Physics (HEP) research is used in the ALICE experiment at European Organization for Nuclear Research CERN. Physicists can submit jobs used to process the huge amount of particle collision data produced by the Large Hadron Collider (LHC). Grids face complex security challenges. They are interesting targets for attackers seeking for huge computational resources. Since users can Execute Arbitrary Code in the worker nodes on the Grid sites, special care should be put in this environment. Automatic tools to harden and monitor this scenario are required. Currently, there is no integrated solution for such requirement. This paper describes a new security framework to allow execution of job payloads in a sandboxed context. It also allows process behavior monitoring to detect intrusions, even when new attack methods or zero day vulnerabilities are exploited, by a Machine Learning approach. We plan to implement the proposed framework as a software prototype that will be tested as a component of the ALICE Grid middleware.Grids allow users flexible on-demand usage of computing resources through remote communication networks. A remarkable example of a Grid in High Energy Physics (HEP) research is used in the ALICE experiment at European Organization for Nuclear Research CERN. Physicists can submit jobs used to process the huge amount of particle collision data produced by the Large Hadron Collider (LHC). Grids face complex security challenges. They are interesting targets for attackers seeking for huge computational resources. Since users can Execute Arbitrary Code in the worker nodes on the Grid sites, special care should be put in this environment. Automatic tools to harden and monitor this scenario are required. Currently, there is no integrated solution for such requirement. This paper describes a new security framework to allow execution of job payloads in a sandboxed context. It also allows process behavior monitoring to detect intrusions, even when new attack methods or zero day vulnerabilities are exploited, by a Machine Learning approach. We plan to implement the proposed framework as a software prototype that will be tested as a component of the ALICE Grid middleware

  • Intrusion Prevention and Detection in Grid Computing - The ALICE Case
    'IOP Publishing', 2017
    Co-Authors: Gomez Andres, Lara Camilo, Kebschull Udo
    Abstract:

    Grids allow users flexible on-demand usage of computing resources through remote communication networks. A remarkable example of a Grid in High Energy Physics (HEP) research is used in the ALICE experiment at European Organization for Nuclear Research CERN. Physicists can submit jobs used to process the huge amount of particle collision data produced by the Large Hadron Collider (LHC). Grids face complex security challenges. They are interesting targets for attackers seeking for huge computational resources. Since users can Execute Arbitrary Code in the worker nodes on the Grid sites, special care should be put in this environment. Automatic tools to harden and monitor this scenario are required. Currently, there is no integrated solution for such requirement. This paper describes a new security framework to allow execution of job payloads in a sandboxed context. It also allows process behavior monitoring to detect intrusions, even when new attack methods or zero day vulnerabilities are exploited, by a Machine Learning approach. We plan to implement the proposed framework as a software prototype that will be tested as a component of the ALICE Grid middleware.Comment: Journal of Physics: Conference Series, Volume 66

Gomez Andres - One of the best experts on this subject based on the ideXlab platform.

  • Intrusion Prevention and Detection in Grid Computing - The ALICE Case
    'IOP Publishing', 2017
    Co-Authors: Gomez Andres, Lara Camilo, Kebschull Udo
    Abstract:

    Grids allow users flexible on-demand usage of computing resources through remote communication networks. A remarkable example of a Grid in High Energy Physics (HEP) research is used in the ALICE experiment at European Organization for Nuclear Research CERN. Physicists can submit jobs used to process the huge amount of particle collision data produced by the Large Hadron Collider (LHC). Grids face complex security challenges. They are interesting targets for attackers seeking for huge computational resources. Since users can Execute Arbitrary Code in the worker nodes on the Grid sites, special care should be put in this environment. Automatic tools to harden and monitor this scenario are required. Currently, there is no integrated solution for such requirement. This paper describes a new security framework to allow execution of job payloads in a sandboxed context. It also allows process behavior monitoring to detect intrusions, even when new attack methods or zero day vulnerabilities are exploited, by a Machine Learning approach. We plan to implement the proposed framework as a software prototype that will be tested as a component of the ALICE Grid middleware.Grids allow users flexible on-demand usage of computing resources through remote communication networks. A remarkable example of a Grid in High Energy Physics (HEP) research is used in the ALICE experiment at European Organization for Nuclear Research CERN. Physicists can submit jobs used to process the huge amount of particle collision data produced by the Large Hadron Collider (LHC). Grids face complex security challenges. They are interesting targets for attackers seeking for huge computational resources. Since users can Execute Arbitrary Code in the worker nodes on the Grid sites, special care should be put in this environment. Automatic tools to harden and monitor this scenario are required. Currently, there is no integrated solution for such requirement. This paper describes a new security framework to allow execution of job payloads in a sandboxed context. It also allows process behavior monitoring to detect intrusions, even when new attack methods or zero day vulnerabilities are exploited, by a Machine Learning approach. We plan to implement the proposed framework as a software prototype that will be tested as a component of the ALICE Grid middleware

  • Intrusion Prevention and Detection in Grid Computing - The ALICE Case
    'IOP Publishing', 2017
    Co-Authors: Gomez Andres, Lara Camilo, Kebschull Udo
    Abstract:

    Grids allow users flexible on-demand usage of computing resources through remote communication networks. A remarkable example of a Grid in High Energy Physics (HEP) research is used in the ALICE experiment at European Organization for Nuclear Research CERN. Physicists can submit jobs used to process the huge amount of particle collision data produced by the Large Hadron Collider (LHC). Grids face complex security challenges. They are interesting targets for attackers seeking for huge computational resources. Since users can Execute Arbitrary Code in the worker nodes on the Grid sites, special care should be put in this environment. Automatic tools to harden and monitor this scenario are required. Currently, there is no integrated solution for such requirement. This paper describes a new security framework to allow execution of job payloads in a sandboxed context. It also allows process behavior monitoring to detect intrusions, even when new attack methods or zero day vulnerabilities are exploited, by a Machine Learning approach. We plan to implement the proposed framework as a software prototype that will be tested as a component of the ALICE Grid middleware.Comment: Journal of Physics: Conference Series, Volume 66

Lara Camilo - One of the best experts on this subject based on the ideXlab platform.

  • Intrusion Prevention and Detection in Grid Computing - The ALICE Case
    'IOP Publishing', 2017
    Co-Authors: Gomez Andres, Lara Camilo, Kebschull Udo
    Abstract:

    Grids allow users flexible on-demand usage of computing resources through remote communication networks. A remarkable example of a Grid in High Energy Physics (HEP) research is used in the ALICE experiment at European Organization for Nuclear Research CERN. Physicists can submit jobs used to process the huge amount of particle collision data produced by the Large Hadron Collider (LHC). Grids face complex security challenges. They are interesting targets for attackers seeking for huge computational resources. Since users can Execute Arbitrary Code in the worker nodes on the Grid sites, special care should be put in this environment. Automatic tools to harden and monitor this scenario are required. Currently, there is no integrated solution for such requirement. This paper describes a new security framework to allow execution of job payloads in a sandboxed context. It also allows process behavior monitoring to detect intrusions, even when new attack methods or zero day vulnerabilities are exploited, by a Machine Learning approach. We plan to implement the proposed framework as a software prototype that will be tested as a component of the ALICE Grid middleware.Grids allow users flexible on-demand usage of computing resources through remote communication networks. A remarkable example of a Grid in High Energy Physics (HEP) research is used in the ALICE experiment at European Organization for Nuclear Research CERN. Physicists can submit jobs used to process the huge amount of particle collision data produced by the Large Hadron Collider (LHC). Grids face complex security challenges. They are interesting targets for attackers seeking for huge computational resources. Since users can Execute Arbitrary Code in the worker nodes on the Grid sites, special care should be put in this environment. Automatic tools to harden and monitor this scenario are required. Currently, there is no integrated solution for such requirement. This paper describes a new security framework to allow execution of job payloads in a sandboxed context. It also allows process behavior monitoring to detect intrusions, even when new attack methods or zero day vulnerabilities are exploited, by a Machine Learning approach. We plan to implement the proposed framework as a software prototype that will be tested as a component of the ALICE Grid middleware

  • Intrusion Prevention and Detection in Grid Computing - The ALICE Case
    'IOP Publishing', 2017
    Co-Authors: Gomez Andres, Lara Camilo, Kebschull Udo
    Abstract:

    Grids allow users flexible on-demand usage of computing resources through remote communication networks. A remarkable example of a Grid in High Energy Physics (HEP) research is used in the ALICE experiment at European Organization for Nuclear Research CERN. Physicists can submit jobs used to process the huge amount of particle collision data produced by the Large Hadron Collider (LHC). Grids face complex security challenges. They are interesting targets for attackers seeking for huge computational resources. Since users can Execute Arbitrary Code in the worker nodes on the Grid sites, special care should be put in this environment. Automatic tools to harden and monitor this scenario are required. Currently, there is no integrated solution for such requirement. This paper describes a new security framework to allow execution of job payloads in a sandboxed context. It also allows process behavior monitoring to detect intrusions, even when new attack methods or zero day vulnerabilities are exploited, by a Machine Learning approach. We plan to implement the proposed framework as a software prototype that will be tested as a component of the ALICE Grid middleware.Comment: Journal of Physics: Conference Series, Volume 66

Pedro Perislopez - One of the best experts on this subject based on the ideXlab platform.

  • avrand a software based defense against Code reuse attacks for avr embedded devices
    International Conference on Detection of intrusions and malware and vulnerability assessment, 2016
    Co-Authors: Sergio Pastrana, Juan E Tapiador, Guillermo Suareztangil, Pedro Perislopez
    Abstract:

    Code reuse attacks are advanced exploitation techniques that constitute a serious threat for modern systems. They profit from a control flow hijacking vulnerability to maliciously Execute one or more pieces of Code from the targeted application. ASLR and Control Flow Integrity are two mechanisms commonly used to deter automated attacks based on Code reuse. Unfortunately, none of these solutions are suitable for modified Harvard architectures such as AVR microcontrollers. In this work, we present a Code reuse attack against embedded AVR devices that shows how an adversary can Execute Arbitrary Code reused from the firmware and other external libraries. We then propose a software-based defense based on fine-grained random permutations of the Code memory. Our solution is installed in the bootloader section of the embedded device and thus Executes during every device reset. We also propose a self-obfuscation technique to hinder Code-reuse attacks against the bootloader.

Jens Grossklags - One of the best experts on this subject based on the ideXlab platform.

  • it s all about the benjamins an empirical study on incentivizing users to ignore security advice
    Financial Cryptography, 2011
    Co-Authors: Nicolas Christin, Serge Egelman, Timothy Vidas, Jens Grossklags
    Abstract:

    We examine the cost for an attacker to pay users to Execute Arbitrary Code--potentially malware. We asked users at home to download and run an executable we wrote without being told what it did and without any way of knowing it was harmless. Each week, we increased the payment amount. Our goal was to examine whether users would ignore common security advice--not to run untrusted executables--if there was a direct incentive, and how much this incentive would need to be. We observed that for payments as low as $0.01, 22% of the people who viewed the task ultimately ran our executable. Once increased to $1.00, this proportion increased to 43%. We show that as the price increased, more and more users who understood the risks ultimately ran the Code. We conclude that users are generally unopposed to running programs of unknown provenance, so long as their incentives exceed their inconvenience.