The Experts below are selected from a list of 8175 Experts worldwide ranked by ideXlab platform
Sajjan G. Shiva - One of the best experts on this subject based on the ideXlab platform.
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Ontology-Based Security Recommendation for the Internet of Medical Things
IEEE Access, 2019Co-Authors: Faisal Alsubaei, Abdullah Abuhussein, Sajjan G. ShivaAbstract:Security and privacy are among the key barriers to adopting the Internet of Medical Things (IoMT) solutions. IoMT adopters have to adhere to Security and privacy policies to ensure that patient data remains confidential and secure. However, there is confusion among IoMT stakeholders as to what Security measures they should expect from the IoMT manufacturers and whether these measures would comply with the adopter's Security and compliance requirements. In this paper, we present a Recommendation tool that models IoMT concepts and Security issues in addition to successively recommending Security measures. The presented tool utilizes semantically enriched ontology to model the IoMT components, Security issues, and measures. The developed ontology is equipped with context-aware rules to enable reasoning in order to build a Recommendation system that empowers users to make well-educated decisions. The Recommendation tool classifies IoMT Security threats faced by IoMT stakeholders and automatically recommends Security controls that have to be enforced for each threat. We have experimented the proposed tool with respect to the completeness and effectiveness of its output (i.e., Security issues and recommended Security measures). The results show that the tool was effectively able to recommend necessary Security measures.
Thomas Jensen - One of the best experts on this subject based on the ideXlab platform.
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Securing Compilation Against Memory Probing
2018Co-Authors: Frédéric Besson, Alexandre Dang, Thomas JensenAbstract:A common Security Recommendation is to reduce the in-memory lifetime of secret values, in order to reduce the risk that an attacker can obtain secret data by probing memory. To mitigate this risk, secret values can be overwritten, at source level, after their last use. The problem we address here is how to ensure that a compiler preserve these mitigation efforts and thus that secret values are not easier to obtain at assembly level than at source level. We propose a formal definition of Information Flow Preserving program Transformations in which we model the information leak of a program using the notion of Attacker Knowledge. Program transformations are validated by relating the knowledge of the attacker before and after the transformation. We consider two classic compiler passes (Dead Store Elimination and Register Allocation) and show how to validate and, if needed, modify these transformations in order to be information flow preserving.
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PLAS@CCS - Securing Compilation Against Memory Probing
Proceedings of the 13th Workshop on Programming Languages and Analysis for Security, 2018Co-Authors: Frédéric Besson, Alexandre Dang, Thomas JensenAbstract:A common Security Recommendation is to reduce the in-memory lifetime of secret values, in order to reduce the risk that an attacker can obtain secret data by probing memory. To mitigate this risk, secret values can be overwritten, at source level, after their last use. The problem we address here is how to ensure that a compiler preserve these mitigation efforts and thus that secret values are not easier to obtain at assembly level than at source level. We propose a formal definition of Information Flow Preserving program Transformations in which we model the information leak of a program using the notion of Attacker Knowledge. Program transformations are validated by relating the knowledge of the attacker before and after the transformation. We consider two classic compiler passes (Dead Store Elimination and Register Allocation) and show how to validate and, if needed, modify these transformations in order to be information flow preserving.
Faisal Alsubaei - One of the best experts on this subject based on the ideXlab platform.
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Ontology-Based Security Recommendation for the Internet of Medical Things
IEEE Access, 2019Co-Authors: Faisal Alsubaei, Abdullah Abuhussein, Sajjan G. ShivaAbstract:Security and privacy are among the key barriers to adopting the Internet of Medical Things (IoMT) solutions. IoMT adopters have to adhere to Security and privacy policies to ensure that patient data remains confidential and secure. However, there is confusion among IoMT stakeholders as to what Security measures they should expect from the IoMT manufacturers and whether these measures would comply with the adopter's Security and compliance requirements. In this paper, we present a Recommendation tool that models IoMT concepts and Security issues in addition to successively recommending Security measures. The presented tool utilizes semantically enriched ontology to model the IoMT components, Security issues, and measures. The developed ontology is equipped with context-aware rules to enable reasoning in order to build a Recommendation system that empowers users to make well-educated decisions. The Recommendation tool classifies IoMT Security threats faced by IoMT stakeholders and automatically recommends Security controls that have to be enforced for each threat. We have experimented the proposed tool with respect to the completeness and effectiveness of its output (i.e., Security issues and recommended Security measures). The results show that the tool was effectively able to recommend necessary Security measures.
Frédéric Besson - One of the best experts on this subject based on the ideXlab platform.
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Securing Compilation Against Memory Probing
2018Co-Authors: Frédéric Besson, Alexandre Dang, Thomas JensenAbstract:A common Security Recommendation is to reduce the in-memory lifetime of secret values, in order to reduce the risk that an attacker can obtain secret data by probing memory. To mitigate this risk, secret values can be overwritten, at source level, after their last use. The problem we address here is how to ensure that a compiler preserve these mitigation efforts and thus that secret values are not easier to obtain at assembly level than at source level. We propose a formal definition of Information Flow Preserving program Transformations in which we model the information leak of a program using the notion of Attacker Knowledge. Program transformations are validated by relating the knowledge of the attacker before and after the transformation. We consider two classic compiler passes (Dead Store Elimination and Register Allocation) and show how to validate and, if needed, modify these transformations in order to be information flow preserving.
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PLAS@CCS - Securing Compilation Against Memory Probing
Proceedings of the 13th Workshop on Programming Languages and Analysis for Security, 2018Co-Authors: Frédéric Besson, Alexandre Dang, Thomas JensenAbstract:A common Security Recommendation is to reduce the in-memory lifetime of secret values, in order to reduce the risk that an attacker can obtain secret data by probing memory. To mitigate this risk, secret values can be overwritten, at source level, after their last use. The problem we address here is how to ensure that a compiler preserve these mitigation efforts and thus that secret values are not easier to obtain at assembly level than at source level. We propose a formal definition of Information Flow Preserving program Transformations in which we model the information leak of a program using the notion of Attacker Knowledge. Program transformations are validated by relating the knowledge of the attacker before and after the transformation. We consider two classic compiler passes (Dead Store Elimination and Register Allocation) and show how to validate and, if needed, modify these transformations in order to be information flow preserving.
Abdullah Abuhussein - One of the best experts on this subject based on the ideXlab platform.
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Ontology-Based Security Recommendation for the Internet of Medical Things
IEEE Access, 2019Co-Authors: Faisal Alsubaei, Abdullah Abuhussein, Sajjan G. ShivaAbstract:Security and privacy are among the key barriers to adopting the Internet of Medical Things (IoMT) solutions. IoMT adopters have to adhere to Security and privacy policies to ensure that patient data remains confidential and secure. However, there is confusion among IoMT stakeholders as to what Security measures they should expect from the IoMT manufacturers and whether these measures would comply with the adopter's Security and compliance requirements. In this paper, we present a Recommendation tool that models IoMT concepts and Security issues in addition to successively recommending Security measures. The presented tool utilizes semantically enriched ontology to model the IoMT components, Security issues, and measures. The developed ontology is equipped with context-aware rules to enable reasoning in order to build a Recommendation system that empowers users to make well-educated decisions. The Recommendation tool classifies IoMT Security threats faced by IoMT stakeholders and automatically recommends Security controls that have to be enforced for each threat. We have experimented the proposed tool with respect to the completeness and effectiveness of its output (i.e., Security issues and recommended Security measures). The results show that the tool was effectively able to recommend necessary Security measures.