The Experts below are selected from a list of 2328 Experts worldwide ranked by ideXlab platform
Elaine B Barker - One of the best experts on this subject based on the ideXlab platform.
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recommendation for the triple data encryption algorithm tdea block cipher nist special publication 800 67 revision 2
2012Co-Authors: William C Barker, Elaine B BarkerAbstract:This National Institute of Standards and Technology Special Publication 800-67, Revision 2: Recommendations for the Triple Data Encryption Algorithm (TDEA) Block Cipher specifies the Triple Data Encryption Algorithm (TDEA), including its primary component Cryptographic engine, the Data Encryption Algorithm (DEA). When implemented in an SP 800-38 series-compliant mode of operation and in a FIPS 140-2 compliant Cryptographic Module, TDEA may be used by Federal organizations to protect sensitive unclassified data.~
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sp 800 67 rev 1 recommendation for the triple data encryption algorithm tdea block cipher
2012Co-Authors: William C Barker, Elaine B BarkerAbstract:This publication specifies the Triple Data Encryption Algorithm (TDEA), including its primary component Cryptographic engine, the Data Encryption Algorithm (DEA). When implemented in an SP 800-38-series-compliant mode of operation and in a FIPS 140-2-compliant Cryptographic Module, TDEA may be used by Federal organizations to protect sensitive unclassified data. Protection of data during transmission or while in storage may be necessary to maintain the confidentiality and integrity of the information represented by the data. This Recommendation defines the mathematical steps required to Cryptographically protect data using TDEA and to subsequently process such protected data. TDEA is made available for use by Federal agencies within the context of a total security program consisting of physical security procedures, good information management practices, and computer system/network access controls.
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sp 800 21 second edition guideline for implementing cryptography in the federal government
2005Co-Authors: Elaine B Barker, William C Barker, Annabelle LeeAbstract:This Second Edition of NIST Special Publication (SP) 800-21, updates and replaces the November 1999 edition of Guideline for Implementing Cryptography in the Federal Government. Many of the references and Cryptographic techniques contained in the first edition of NIST SP 800-21 have been amended, rescinded, or superseded since its publication. The current publication offers new tools and techniques. NIST SP 800-21 is intended to provide a structured, yet flexible set of guidelines for selecting, specifying, employing, and evaluating Cryptographic protection mechanisms in Federal information systems?and thus, makes a significant contribution toward satisfying the security requirements of the Federal Information Security Management Act (FISMA) of 2002, Public Law 107-347. The current publication also reflects the elimination of the waiver process by the Federal Information Security Management Act (FISMA) of 2002. SP 800-21 includes background information, describes the advantages of using cryptography; defines the role and use of standards and describes standards organizations that are outside the Federal government; describes the methods that are available for symmetric and asymmetric key cryptography; describes implementation issues (e.g., key management); discusses assessments, including the Cryptographic Module Validation Program (CMVP), the Common Criteria (CC), and Certification and Accreditation (CA and describes the process of choosing the types of cryptography to be used and selecting a Cryptographic method or methods to fulfill a specific requirement.
Takeshi Fujino - One of the best experts on this subject based on the ideXlab platform.
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deep learning side channel attack against hardware implementations of aes
Microprocessors and Microsystems, 2020Co-Authors: Takaya Kubota, Kota Yoshida, Mitsuru Shiozaki, Takeshi FujinoAbstract:Abstract In the field of image recognition, machine learning technologies, especially deep learning, have been rapidly advancing alongside the advances of hardware such as GPUs. In image recognition, in general, large numbers of labeled images to be identified are input to a neural network, and repeatedly learning the images enables the neural network to identify objects with high accuracy. A new profiling side-channel attack method, the deep learning side-channel attack (DL-SCA), utilizes the neural network’s high identifying ability to unveil a Cryptographic Module’s secret key from side-channel information. In DL-SCAs, the neural network is trained with power waveforms captured from a target Cryptographic Module, and the trained network extracts the leaky part that depends on the secret. However, at this stage, the main target of investigation has been software implementation, and studies regarding hardware implementation, such as ASIC, are somewhat lacking. In this paper, we first depict deep learning techniques, profiling side-channel attacks, and leak models to clarify the relation between secret and side channels. Next, we investigate the use of DL-SCA against hardware implementations of AES and discuss the problem derived from the Hamming distance model and ShiftRow operation of AES. To solve the problem, we propose a new network training method called “mixed model dataset based on round-round XORed value.” We prove that our proposal solves the problem and gives the attack capability to neural networks. We also compare the attack performance and characteristics of DL-SCA to conventional analysis methods such as correlation power analysis and conventional template attack. In our experiment, a dedicated ASIC chip for side-channel analysis is utilized and the chip is also equipped with a side-channel countermeasure AES. We show how DL-SCA can recover secret keys against the side-channel countermeasure circuit. Our results demonstrate that DL-SCA can be a more powerful option against side-channel countermeasure implementations than conventional SCAs.
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deep learning side channel attack against hardware implementations of aes
Digital Systems Design, 2019Co-Authors: Takaya Kubota, Kota Yoshida, Mitsuru Shiozaki, Takeshi FujinoAbstract:In the field of image recognition, machine learning technologies, especially deep learning, have been rapidly advancing alongside with the advances of hardware such as GPUs. In image recognition, in general, large numbers of labeled sets containing image and correct value pairs to be identified are input to a neural network, and repeatedly learning the set enables the neural network to identify objects with high accuracy. A new side-channel attack method, deep learning side-channel attack (DLSCA), utilizes the high identifying ability of the neural network to try and unveil a secret key of the Cryptographic Module by being trained with power waveforms and learning the leak model. However, at this stage, attacks on software implementations have been mainly investigated. In contrast, there are few studies about hardware implementations especially such as ASIC circuits. In this paper, we investigate the use of DL-SCA against hardware implementations of AES and demonstrate that it is able to unveil the secret key by applying a new technique named "mixed model dataset based on round-round XORed value." We also compare the attack performance and characteristics of DL-SCA with conventional analysis methods such as correlation power analysis and conventional template attack.
William C Barker - One of the best experts on this subject based on the ideXlab platform.
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recommendation for the triple data encryption algorithm tdea block cipher nist special publication 800 67 revision 2
2012Co-Authors: William C Barker, Elaine B BarkerAbstract:This National Institute of Standards and Technology Special Publication 800-67, Revision 2: Recommendations for the Triple Data Encryption Algorithm (TDEA) Block Cipher specifies the Triple Data Encryption Algorithm (TDEA), including its primary component Cryptographic engine, the Data Encryption Algorithm (DEA). When implemented in an SP 800-38 series-compliant mode of operation and in a FIPS 140-2 compliant Cryptographic Module, TDEA may be used by Federal organizations to protect sensitive unclassified data.~
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sp 800 67 rev 1 recommendation for the triple data encryption algorithm tdea block cipher
2012Co-Authors: William C Barker, Elaine B BarkerAbstract:This publication specifies the Triple Data Encryption Algorithm (TDEA), including its primary component Cryptographic engine, the Data Encryption Algorithm (DEA). When implemented in an SP 800-38-series-compliant mode of operation and in a FIPS 140-2-compliant Cryptographic Module, TDEA may be used by Federal organizations to protect sensitive unclassified data. Protection of data during transmission or while in storage may be necessary to maintain the confidentiality and integrity of the information represented by the data. This Recommendation defines the mathematical steps required to Cryptographically protect data using TDEA and to subsequently process such protected data. TDEA is made available for use by Federal agencies within the context of a total security program consisting of physical security procedures, good information management practices, and computer system/network access controls.
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sp 800 21 second edition guideline for implementing cryptography in the federal government
2005Co-Authors: Elaine B Barker, William C Barker, Annabelle LeeAbstract:This Second Edition of NIST Special Publication (SP) 800-21, updates and replaces the November 1999 edition of Guideline for Implementing Cryptography in the Federal Government. Many of the references and Cryptographic techniques contained in the first edition of NIST SP 800-21 have been amended, rescinded, or superseded since its publication. The current publication offers new tools and techniques. NIST SP 800-21 is intended to provide a structured, yet flexible set of guidelines for selecting, specifying, employing, and evaluating Cryptographic protection mechanisms in Federal information systems?and thus, makes a significant contribution toward satisfying the security requirements of the Federal Information Security Management Act (FISMA) of 2002, Public Law 107-347. The current publication also reflects the elimination of the waiver process by the Federal Information Security Management Act (FISMA) of 2002. SP 800-21 includes background information, describes the advantages of using cryptography; defines the role and use of standards and describes standards organizations that are outside the Federal government; describes the methods that are available for symmetric and asymmetric key cryptography; describes implementation issues (e.g., key management); discusses assessments, including the Cryptographic Module Validation Program (CMVP), the Common Criteria (CC), and Certification and Accreditation (CA and describes the process of choosing the types of cryptography to be used and selecting a Cryptographic method or methods to fulfill a specific requirement.
R. Guinee - One of the best experts on this subject based on the ideXlab platform.
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Experimental validation of a novel chaotic circuit for true random binary digit generation in Cryptographic Module application
2009 Ph.D. Research in Microelectronics and Electronics, 2009Co-Authors: Maria Blaszczyk, R. GuineeAbstract:In this paper the experimental validation of a novel, modified double scroll chaotic attractor circuit, employed as a true random binary generator (TRBG) is presented. The double scroll attractor is modeled on a chaotic circuit for nonlinear operation leading to stochastic like behavior. The output from the chaotic circuit which is a correlated binary sequence is scrambled with a pseudo random binary sequence generator (PRBSG) topology to yield a true random binary source for key stream generation. The modified chaotic circuit has been first modeled in PSpice software and its state space formulation was implemented in Matlab and Simulink software to gauge simulation accuracy and potential as a Cryptographic Module via statistical testing. The randomness attributes of the modified generator, obtained from both the PSpice state space model along with the hardware implementation, using the PRBSG de-correlator were successfully tested by the well known NIST Test Suite and Diehard Test Set for statistical validation. A physical TRBG has been constructed on the basis of the proposed PRBSG modification with all statistical tests successfully passed confirming theoretical expectations.
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experimental validation of a true random binary digit generator fusion with a pseudo random number generator for Cryptographic Module application
Irish Signals and Systems Conference, 2009Co-Authors: Maria Blaszczyk, R. GuineeAbstract:In this paper the experimental validation of a novel, modified double scroll chaotic attractor circuit, employed as a true random binary generator (TRBG) is presented. The double scroll attractor is modeled on Chua's circuit constituted as independent chaotic oscillator using passive only nonlinear device for nonlinear operation leading to chaotic behavior. The output from the chaotic circuit which is a correlated binary sequence is scrambled with a pseudo random binary sequence generator (PRBSG) topology to yield a true random binary source for key stream generation. The modified chaotic circuit has been first modeled in PSpice software. The randomness attributes of the modified generator, obtained from both the PSpice model and hardware implementation, using the PRBSG de-correlator were successfully tested by the well known NIST Test Suite and Diehard Test Set for statistical validation. Output binary streams from the proposed modified generator were examined for randomness using both Test Suites with all tests successfully passed for both PSpice model and experimental chaotic circuit generator. A physical TRBG has been constructed on the basis of the proposed PRBSG modification with all statistical tests successfully passed confirming theoretical expectations. (6 pages)
Takaya Kubota - One of the best experts on this subject based on the ideXlab platform.
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deep learning side channel attack against hardware implementations of aes
Microprocessors and Microsystems, 2020Co-Authors: Takaya Kubota, Kota Yoshida, Mitsuru Shiozaki, Takeshi FujinoAbstract:Abstract In the field of image recognition, machine learning technologies, especially deep learning, have been rapidly advancing alongside the advances of hardware such as GPUs. In image recognition, in general, large numbers of labeled images to be identified are input to a neural network, and repeatedly learning the images enables the neural network to identify objects with high accuracy. A new profiling side-channel attack method, the deep learning side-channel attack (DL-SCA), utilizes the neural network’s high identifying ability to unveil a Cryptographic Module’s secret key from side-channel information. In DL-SCAs, the neural network is trained with power waveforms captured from a target Cryptographic Module, and the trained network extracts the leaky part that depends on the secret. However, at this stage, the main target of investigation has been software implementation, and studies regarding hardware implementation, such as ASIC, are somewhat lacking. In this paper, we first depict deep learning techniques, profiling side-channel attacks, and leak models to clarify the relation between secret and side channels. Next, we investigate the use of DL-SCA against hardware implementations of AES and discuss the problem derived from the Hamming distance model and ShiftRow operation of AES. To solve the problem, we propose a new network training method called “mixed model dataset based on round-round XORed value.” We prove that our proposal solves the problem and gives the attack capability to neural networks. We also compare the attack performance and characteristics of DL-SCA to conventional analysis methods such as correlation power analysis and conventional template attack. In our experiment, a dedicated ASIC chip for side-channel analysis is utilized and the chip is also equipped with a side-channel countermeasure AES. We show how DL-SCA can recover secret keys against the side-channel countermeasure circuit. Our results demonstrate that DL-SCA can be a more powerful option against side-channel countermeasure implementations than conventional SCAs.
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deep learning side channel attack against hardware implementations of aes
Digital Systems Design, 2019Co-Authors: Takaya Kubota, Kota Yoshida, Mitsuru Shiozaki, Takeshi FujinoAbstract:In the field of image recognition, machine learning technologies, especially deep learning, have been rapidly advancing alongside with the advances of hardware such as GPUs. In image recognition, in general, large numbers of labeled sets containing image and correct value pairs to be identified are input to a neural network, and repeatedly learning the set enables the neural network to identify objects with high accuracy. A new side-channel attack method, deep learning side-channel attack (DLSCA), utilizes the high identifying ability of the neural network to try and unveil a secret key of the Cryptographic Module by being trained with power waveforms and learning the leak model. However, at this stage, attacks on software implementations have been mainly investigated. In contrast, there are few studies about hardware implementations especially such as ASIC circuits. In this paper, we investigate the use of DL-SCA against hardware implementations of AES and demonstrate that it is able to unveil the secret key by applying a new technique named "mixed model dataset based on round-round XORed value." We also compare the attack performance and characteristics of DL-SCA with conventional analysis methods such as correlation power analysis and conventional template attack.
