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Advanced encryption standard--AES : 4th international conference, AES 2004, Bonn, Germany, May 10-12, 2004 : revised selected and invited papers / / Hans Dobbertin, Vincent Rijmen, Aleksandra Sowa (eds.)
| Advanced encryption standard--AES : 4th international conference, AES 2004, Bonn, Germany, May 10-12, 2004 : revised selected and invited papers / / Hans Dobbertin, Vincent Rijmen, Aleksandra Sowa (eds.) |
| Edizione | [1st ed. 2005.] |
| Pubbl/distr/stampa | Berlin ; ; New York, : Springer, 2005 |
| Descrizione fisica | 1 online resource (X, 190 p.) |
| Disciplina | 005.8 |
| Altri autori (Persone) |
DobbertinHans
RijmenVincent <1970-> SowaAleksandra |
| Collana | Lecture notes in computer science |
| Soggetto topico |
Computers - Access control - Standards
Data encryption (Computer science) - Standards Computer security - Standards |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Cryptanalytic Attacks and Related Results -- The Cryptanalysis of the AES – A Brief Survey -- The Boomerang Attack on 5 and 6-Round Reduced AES -- A Three Rounds Property of the AES -- DFA on AES -- Refined Analysis of Bounds Related to Linear and Differential Cryptanalysis for the AES -- Algebraic Attacks and Related Results -- Some Algebraic Aspects of the Advanced Encryption Standard -- General Principles of Algebraic Attacks and New Design Criteria for Cipher Components -- An Algebraic Interpretation of 128 -- Hardware Implementations -- Efficient AES Implementations on ASICs and FPGAs -- Small Size, Low Power, Side Channel-Immune AES Coprocessor: Design and Synthesis Results -- Other Topics -- Complementation-Like and Cyclic Properties of AES Round Functions -- More Dual Rijndaels -- Representations and Rijndael Descriptions -- Linearity of the AES Key Schedule -- The Inverse S-Box, Non-linear Polynomial Relations and Cryptanalysis of Block Ciphers. |
| Altri titoli varianti |
Advanced encryption standard
AES AES 2004 |
| Record Nr. | UNINA-9910484780103321 |
| Berlin ; ; New York, : Springer, 2005 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Android malware detection using machine learning : data-driven fingerprinting and threat intelligence / / ElMouatez Billah Karbab [and three others]
| Android malware detection using machine learning : data-driven fingerprinting and threat intelligence / / ElMouatez Billah Karbab [and three others] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (212 pages) |
| Disciplina | 005.8 |
| Collana | Advances in Information Security |
| Soggetto topico |
Malware (Computer software) - Prevention
Computer security - Standards |
| ISBN | 3-030-74664-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Contents -- List of Figures -- List of Tables -- 1 Introduction -- 1.1 Motivations -- 1.2 Objectives -- 1.3 Research Contributions -- 1.4 Book Organization -- References -- 2 Background and Related Work -- 2.1 Background -- 2.1.1 Android OS Overview -- 2.1.1.1 Android Apk Format -- 2.1.1.2 Android Markets -- 2.1.2 Android Security -- 2.1.2.1 Android Security Threats -- 2.1.2.2 Design Challenges of Malware Detection Systems -- 2.2 Android Malware Detection Overview -- 2.3 Taxonomy of Android Malware Detection Systems -- 2.3.1 Malware Threats -- 2.3.2 Detection System Deployment -- 2.4 Performance Criteria for Malware Detection -- 2.4.1 Feature Selection -- 2.4.2 Detection Strategy -- 2.5 General Malware Threat Detection -- 2.5.1 Workstation-Based Solutions -- 2.5.2 Mobile-Based Solutions -- 2.5.3 Hybrid Solutions -- 2.5.4 Discussions -- 2.6 Specific Malware Threat Detection -- 2.6.1 Workstation-Based Solutions -- 2.6.2 Mobile-Based Solutions -- 2.6.3 Hybrid Solutions -- 2.6.4 Discussions -- 2.7 Android Malware Detection Helpers -- 2.7.1 Discussions -- 2.8 Summary -- References -- 3 Fingerprinting Android Malware Packages -- 3.1 Approximate Static Fingerprint -- 3.1.1 Fingerprint Structure -- 3.1.2 Fingerprints Generation -- 3.1.2.1 N-grams -- 3.1.2.2 Feature Hashing -- 3.1.2.3 Fingerprint Computation Process -- 3.1.2.4 Compute Fingerprints Similarity -- 3.2 Malware Detection Framework -- 3.2.1 Peer-Fingerprint Voting -- 3.2.2 Peer-Matching -- 3.2.2.1 Family-Fingerprinting -- 3.3 Experimental Results -- 3.3.1 Testing Setup -- 3.3.2 Evaluation Results -- 3.3.2.1 Family-Fingerprinting Results -- 3.3.2.2 Peer-Matching Results -- 3.3.2.3 Peer-Voting vs Merged Fingerprints -- 3.3.3 Discussion -- 3.4 Summary -- References -- 4 Robust Android Malicious Community Fingerprinting -- 4.1 Threat Model -- 4.2 Usage Scenarios -- 4.3 Clustering Process.
4.4 Static Features -- 4.4.1 N-grams -- 4.4.1.1 Classes.dex Byte N-grams -- 4.4.1.2 Assembly Opcodes N-grams -- 4.4.2 Native Library N-grams -- 4.4.2.1 APK N-grams -- 4.4.3 Manifest File Features -- 4.4.4 Android API Calls -- 4.4.5 Resources -- 4.4.6 APK Content Types -- 4.4.7 Feature Preprocessing -- 4.5 LSH Similarity Computation -- 4.6 Community Detection -- 4.7 Community Fingerprint -- 4.8 Experimental Results -- 4.8.1 Dataset and Test Setup -- 4.8.1.1 App Detection Metrics -- 4.8.1.2 Community Detection Metrics -- 4.8.2 Mixed Dataset Results -- 4.8.3 Results of Malware-Only Datasets -- 4.8.4 Community Fingerprint Results -- 4.9 Hyper-Parameter Analyses -- 4.9.1 Purity Analysis -- 4.9.2 Coverage Analysis -- 4.9.3 Number of Communities Analysis -- 4.9.4 Efficiency Analysis -- 4.10 Case Study: Recall and Precision Settings -- 4.11 Case Study: Obfuscation -- 4.12 Summary -- References -- 5 Android Malware Fingerprinting Using Dynamic Analysis -- 5.1 Threat Model -- 5.2 Overview -- 5.2.1 Notation -- 5.3 Methodology -- 5.3.1 Behavioral Reports Generation -- 5.3.2 Report Vectorization -- 5.3.3 Build Models -- 5.3.4 Ensemble Composition -- 5.3.5 Ensemble Prediction Process -- 5.4 MalDy Framework -- 5.4.1 Machine Learning Algorithms -- 5.5 Evaluation Results -- 5.5.1 Evaluation Datasets -- 5.5.2 Effectiveness -- 5.5.2.1 Classifier Effect -- 5.5.2.2 Effect of the Vectorization Technique -- 5.5.2.3 Effect of Tuning Hyper-Parameters -- 5.5.3 Portability -- 5.5.3.1 MalDy on Win32 Malware -- 5.5.3.2 MalDy Train Dataset Size -- 5.5.4 Efficiency -- 5.6 Summary -- References -- 6 Fingerprinting Cyber-Infrastructures of Android Malware -- 6.1 Threat Model -- 6.2 Usage Scenarios -- 6.3 Methodology -- 6.3.1 Threat Communities Detection -- 6.3.2 Action Prioritization -- 6.3.2.1 PageRank Algorithm -- 6.3.3 Security Correlation. 6.3.3.1 Network Enrichment Using Passive DNS -- 6.3.3.2 Threat Network Tagging -- 6.4 Experimental Results -- 6.4.1 Android Malware Dataset -- 6.4.2 Implementation -- 6.4.3 Drebin Threat Network -- 6.4.4 Family Threat Networks -- 6.5 Summary -- References -- 7 Portable Supervised Malware Fingerprinting Using Deep Learning -- 7.1 Threat Model -- 7.2 Usage Scenarios -- 7.3 Methodology -- 7.3.1 MalDozer Method Embedding -- 7.3.2 MalDozer Neural Network -- 7.3.3 Implementation -- 7.4 Evaluation -- 7.4.1 Datasets -- 7.4.2 Malware Detection Performance -- 7.4.2.1 Unknown Malware Detection -- 7.4.2.2 Resiliency Against API Evolution Over Time -- 7.4.2.3 Resiliency Against Changing the Order of API Methods -- 7.4.3 Family Attribution Performance -- 7.4.4 Runtime Performance -- 7.4.4.1 Model Complexity Evaluation -- 7.5 Summary -- References -- 8 Resilient and Adaptive Android Malware Fingerprinting and Detection -- 8.1 Methodology -- 8.1.1 Approach -- 8.1.2 Android App Representation -- 8.1.3 Malware Detection -- 8.1.3.1 Fragment Detection -- 8.1.3.2 Inst2Vec Embedding -- 8.1.3.3 Classification Model -- 8.1.3.4 Dataset Notation -- 8.1.3.5 Detection Ensemble -- 8.1.3.6 Confidence Analysis -- 8.1.3.7 PetaDroid Adaptation Mechanism -- 8.1.4 Malware Clustering -- 8.1.4.1 InstNGram2Vec -- 8.1.4.2 Deep Neural Auto-Encoder and Digest Generation -- 8.1.4.3 Malware Family Clustering -- 8.1.5 Implementation -- 8.2 Evaluation -- 8.2.1 Android Dataset -- 8.2.2 Malware Detection -- 8.2.2.1 Detection Performance -- 8.2.2.2 Dataset Size Effect -- 8.2.2.3 Ensemble Size Effect -- 8.2.3 Family Clustering -- 8.2.4 Obfuscation Resiliency -- 8.2.5 Change Over Time Resiliency -- 8.2.6 PetaDroid Automatic Adaptation -- 8.2.7 Efficiency -- 8.3 Comparative Study -- 8.3.1 Detection Performance Comparison -- 8.3.2 Efficiency Comparison -- 8.3.3 Time Resiliency Comparison. 8.4 Case Studies -- 8.4.1 Scalable Detection -- 8.4.2 Scalable Automatic Adaptation -- 8.5 Summary -- References -- 9 Conclusion -- 9.1 Concluding Remarks -- 9.2 Lessons Learned -- 9.3 Future Research Directions -- References -- Index. |
| Record Nr. | UNISA-996464514303316 |
| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Android malware detection using machine learning : data-driven fingerprinting and threat intelligence / / ElMouatez Billah Karbab [and three others]
| Android malware detection using machine learning : data-driven fingerprinting and threat intelligence / / ElMouatez Billah Karbab [and three others] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (212 pages) |
| Disciplina | 005.8 |
| Collana | Advances in Information Security |
| Soggetto topico |
Malware (Computer software) - Prevention
Computer security - Standards |
| ISBN | 3-030-74664-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Contents -- List of Figures -- List of Tables -- 1 Introduction -- 1.1 Motivations -- 1.2 Objectives -- 1.3 Research Contributions -- 1.4 Book Organization -- References -- 2 Background and Related Work -- 2.1 Background -- 2.1.1 Android OS Overview -- 2.1.1.1 Android Apk Format -- 2.1.1.2 Android Markets -- 2.1.2 Android Security -- 2.1.2.1 Android Security Threats -- 2.1.2.2 Design Challenges of Malware Detection Systems -- 2.2 Android Malware Detection Overview -- 2.3 Taxonomy of Android Malware Detection Systems -- 2.3.1 Malware Threats -- 2.3.2 Detection System Deployment -- 2.4 Performance Criteria for Malware Detection -- 2.4.1 Feature Selection -- 2.4.2 Detection Strategy -- 2.5 General Malware Threat Detection -- 2.5.1 Workstation-Based Solutions -- 2.5.2 Mobile-Based Solutions -- 2.5.3 Hybrid Solutions -- 2.5.4 Discussions -- 2.6 Specific Malware Threat Detection -- 2.6.1 Workstation-Based Solutions -- 2.6.2 Mobile-Based Solutions -- 2.6.3 Hybrid Solutions -- 2.6.4 Discussions -- 2.7 Android Malware Detection Helpers -- 2.7.1 Discussions -- 2.8 Summary -- References -- 3 Fingerprinting Android Malware Packages -- 3.1 Approximate Static Fingerprint -- 3.1.1 Fingerprint Structure -- 3.1.2 Fingerprints Generation -- 3.1.2.1 N-grams -- 3.1.2.2 Feature Hashing -- 3.1.2.3 Fingerprint Computation Process -- 3.1.2.4 Compute Fingerprints Similarity -- 3.2 Malware Detection Framework -- 3.2.1 Peer-Fingerprint Voting -- 3.2.2 Peer-Matching -- 3.2.2.1 Family-Fingerprinting -- 3.3 Experimental Results -- 3.3.1 Testing Setup -- 3.3.2 Evaluation Results -- 3.3.2.1 Family-Fingerprinting Results -- 3.3.2.2 Peer-Matching Results -- 3.3.2.3 Peer-Voting vs Merged Fingerprints -- 3.3.3 Discussion -- 3.4 Summary -- References -- 4 Robust Android Malicious Community Fingerprinting -- 4.1 Threat Model -- 4.2 Usage Scenarios -- 4.3 Clustering Process.
4.4 Static Features -- 4.4.1 N-grams -- 4.4.1.1 Classes.dex Byte N-grams -- 4.4.1.2 Assembly Opcodes N-grams -- 4.4.2 Native Library N-grams -- 4.4.2.1 APK N-grams -- 4.4.3 Manifest File Features -- 4.4.4 Android API Calls -- 4.4.5 Resources -- 4.4.6 APK Content Types -- 4.4.7 Feature Preprocessing -- 4.5 LSH Similarity Computation -- 4.6 Community Detection -- 4.7 Community Fingerprint -- 4.8 Experimental Results -- 4.8.1 Dataset and Test Setup -- 4.8.1.1 App Detection Metrics -- 4.8.1.2 Community Detection Metrics -- 4.8.2 Mixed Dataset Results -- 4.8.3 Results of Malware-Only Datasets -- 4.8.4 Community Fingerprint Results -- 4.9 Hyper-Parameter Analyses -- 4.9.1 Purity Analysis -- 4.9.2 Coverage Analysis -- 4.9.3 Number of Communities Analysis -- 4.9.4 Efficiency Analysis -- 4.10 Case Study: Recall and Precision Settings -- 4.11 Case Study: Obfuscation -- 4.12 Summary -- References -- 5 Android Malware Fingerprinting Using Dynamic Analysis -- 5.1 Threat Model -- 5.2 Overview -- 5.2.1 Notation -- 5.3 Methodology -- 5.3.1 Behavioral Reports Generation -- 5.3.2 Report Vectorization -- 5.3.3 Build Models -- 5.3.4 Ensemble Composition -- 5.3.5 Ensemble Prediction Process -- 5.4 MalDy Framework -- 5.4.1 Machine Learning Algorithms -- 5.5 Evaluation Results -- 5.5.1 Evaluation Datasets -- 5.5.2 Effectiveness -- 5.5.2.1 Classifier Effect -- 5.5.2.2 Effect of the Vectorization Technique -- 5.5.2.3 Effect of Tuning Hyper-Parameters -- 5.5.3 Portability -- 5.5.3.1 MalDy on Win32 Malware -- 5.5.3.2 MalDy Train Dataset Size -- 5.5.4 Efficiency -- 5.6 Summary -- References -- 6 Fingerprinting Cyber-Infrastructures of Android Malware -- 6.1 Threat Model -- 6.2 Usage Scenarios -- 6.3 Methodology -- 6.3.1 Threat Communities Detection -- 6.3.2 Action Prioritization -- 6.3.2.1 PageRank Algorithm -- 6.3.3 Security Correlation. 6.3.3.1 Network Enrichment Using Passive DNS -- 6.3.3.2 Threat Network Tagging -- 6.4 Experimental Results -- 6.4.1 Android Malware Dataset -- 6.4.2 Implementation -- 6.4.3 Drebin Threat Network -- 6.4.4 Family Threat Networks -- 6.5 Summary -- References -- 7 Portable Supervised Malware Fingerprinting Using Deep Learning -- 7.1 Threat Model -- 7.2 Usage Scenarios -- 7.3 Methodology -- 7.3.1 MalDozer Method Embedding -- 7.3.2 MalDozer Neural Network -- 7.3.3 Implementation -- 7.4 Evaluation -- 7.4.1 Datasets -- 7.4.2 Malware Detection Performance -- 7.4.2.1 Unknown Malware Detection -- 7.4.2.2 Resiliency Against API Evolution Over Time -- 7.4.2.3 Resiliency Against Changing the Order of API Methods -- 7.4.3 Family Attribution Performance -- 7.4.4 Runtime Performance -- 7.4.4.1 Model Complexity Evaluation -- 7.5 Summary -- References -- 8 Resilient and Adaptive Android Malware Fingerprinting and Detection -- 8.1 Methodology -- 8.1.1 Approach -- 8.1.2 Android App Representation -- 8.1.3 Malware Detection -- 8.1.3.1 Fragment Detection -- 8.1.3.2 Inst2Vec Embedding -- 8.1.3.3 Classification Model -- 8.1.3.4 Dataset Notation -- 8.1.3.5 Detection Ensemble -- 8.1.3.6 Confidence Analysis -- 8.1.3.7 PetaDroid Adaptation Mechanism -- 8.1.4 Malware Clustering -- 8.1.4.1 InstNGram2Vec -- 8.1.4.2 Deep Neural Auto-Encoder and Digest Generation -- 8.1.4.3 Malware Family Clustering -- 8.1.5 Implementation -- 8.2 Evaluation -- 8.2.1 Android Dataset -- 8.2.2 Malware Detection -- 8.2.2.1 Detection Performance -- 8.2.2.2 Dataset Size Effect -- 8.2.2.3 Ensemble Size Effect -- 8.2.3 Family Clustering -- 8.2.4 Obfuscation Resiliency -- 8.2.5 Change Over Time Resiliency -- 8.2.6 PetaDroid Automatic Adaptation -- 8.2.7 Efficiency -- 8.3 Comparative Study -- 8.3.1 Detection Performance Comparison -- 8.3.2 Efficiency Comparison -- 8.3.3 Time Resiliency Comparison. 8.4 Case Studies -- 8.4.1 Scalable Detection -- 8.4.2 Scalable Automatic Adaptation -- 8.5 Summary -- References -- 9 Conclusion -- 9.1 Concluding Remarks -- 9.2 Lessons Learned -- 9.3 Future Research Directions -- References -- Index. |
| Record Nr. | UNINA-9910492141603321 |
| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Automotive cybersecurity : an introduction to ISO/SAE 21434 / / by Dr. David Ward and Paul Wooderson
| Automotive cybersecurity : an introduction to ISO/SAE 21434 / / by Dr. David Ward and Paul Wooderson |
| Autore | Ward David D (Electronics engineer) |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Warrendale, Pennsylvania : , : SAE International, , 2021 |
| Descrizione fisica | 1 online resource (1 PDF (xii, 93 pages)) : color illustrations |
| Disciplina | 629.2826 |
| Soggetto topico |
Automotive computers - Security measures
Computer security - Standards COMPUTERS / Security / General TECHNOLOGY & ENGINEERING / Automotive TRANSPORTATION / Automotive / General Computer security Automotive technology and trades Road and motor vehicles: general interest |
| ISBN |
1-4686-0083-4
1-4686-0081-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Preface -- About the authors -- Chapter 1: Introduction to automotive cybersecurity -- Chapter 2: Cybersecurity for automotive cyber-physical systems -- Chapter 3: Establishing a cybersecurity process -- Chapter 4: Assurance and certification -- Chaper 5: Conclusions and going further -- References -- Index. |
| Record Nr. | UNINA-9910795798803321 |
Ward David D (Electronics engineer)
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| Warrendale, Pennsylvania : , : SAE International, , 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Automotive cybersecurity : an introduction to ISO/SAE 21434 / / by Dr. David Ward and Paul Wooderson
| Automotive cybersecurity : an introduction to ISO/SAE 21434 / / by Dr. David Ward and Paul Wooderson |
| Autore | Ward David D (Electronics engineer) |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Warrendale, Pennsylvania : , : SAE International, , 2021 |
| Descrizione fisica | 1 online resource (1 PDF (xii, 93 pages)) : color illustrations |
| Disciplina | 629.2826 |
| Soggetto topico |
Automotive computers - Security measures
Computer security - Standards COMPUTERS / Security / General TECHNOLOGY & ENGINEERING / Automotive TRANSPORTATION / Automotive / General Computer security Automotive technology and trades Road and motor vehicles: general interest |
| ISBN |
1-4686-0083-4
1-4686-0081-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Preface -- About the authors -- Chapter 1: Introduction to automotive cybersecurity -- Chapter 2: Cybersecurity for automotive cyber-physical systems -- Chapter 3: Establishing a cybersecurity process -- Chapter 4: Assurance and certification -- Chaper 5: Conclusions and going further -- References -- Index. |
| Record Nr. | UNINA-9910826019903321 |
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Ward David D (Electronics engineer)
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| Warrendale, Pennsylvania : , : SAE International, , 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Cloud computing [[electronic resource] ] : an overview of the technology and the issues facing American innovators : hearing before the Subcommittee on Intellectual Property, Competition, and the Internet of the Committee on the Judiciary, House of Representatives, One Hundred Twelfth Congress, second session, July 25, 2012
| Cloud computing [[electronic resource] ] : an overview of the technology and the issues facing American innovators : hearing before the Subcommittee on Intellectual Property, Competition, and the Internet of the Committee on the Judiciary, House of Representatives, One Hundred Twelfth Congress, second session, July 25, 2012 |
| Pubbl/distr/stampa | Washington : , : U.S. G.P.O., , 2012 |
| Descrizione fisica | 1 online resource (iv, 152 pages) : illustrations |
| Soggetto topico |
Cloud computing
Cloud computing - Security measures - United States Computer security - Standards Electronic information resources - Access control Web services - Security measures - United States Computer networks - Security measures - United States Data protection - United States |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Altri titoli varianti | Cloud computing |
| Record Nr. | UNINA-9910702143403321 |
| Washington : , : U.S. G.P.O., , 2012 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Cryptographic algorithms and key sizes for personal identity verification [[electronic resource] /] / W. Timothy Polk, Donna F. Dodson, William E. Burr
| Cryptographic algorithms and key sizes for personal identity verification [[electronic resource] /] / W. Timothy Polk, Donna F. Dodson, William E. Burr |
| Autore | Polk William T |
| Edizione | [Draft.] |
| Pubbl/distr/stampa | Gaithersburg, MD : , : U.S. Dept. of Commerce, Technology Administration, National Institute of Standards and Technology, , [2005] |
| Descrizione fisica | 103 unnumbered pages : digital, PDF file |
| Altri autori (Persone) |
DodsonDonna F
BurrWilliam E |
| Collana | NIST special publication |
| Soggetto topico |
Computer security - Standards
Data encryption (Computer science) |
| Soggetto non controllato |
Conformance test
Cryptographic algorithms FIPS 201 Key sizes Personal Identity Verification PKI |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910695198203321 |
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Polk William T
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| Gaithersburg, MD : , : U.S. Dept. of Commerce, Technology Administration, National Institute of Standards and Technology, , [2005] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Designing to FIPS-140 : A Guide for Engineers and Programmers
| Designing to FIPS-140 : A Guide for Engineers and Programmers |
| Autore | Johnston David |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Berkeley, CA : , : Apress L. P., , 2024 |
| Descrizione fisica | 1 online resource (224 pages) |
| Disciplina | 005.8/24 |
| Altri autori (Persone) | FantRichard |
| Soggetto topico |
Data encryption (Computer science)
Cryptography Computer security - Standards |
| ISBN | 9798868801259 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910855395803321 |
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Johnston David
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| Berkeley, CA : , : Apress L. P., , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Guidance for securing Microsoft Windows XP Home Edition : a NIST security configuration checklist : recommendations of the National Institute of Standards and Technology / / Karen Kent, Murugiah Souppaya, John Connor
| Guidance for securing Microsoft Windows XP Home Edition : a NIST security configuration checklist : recommendations of the National Institute of Standards and Technology / / Karen Kent, Murugiah Souppaya, John Connor |
| Pubbl/distr/stampa | [Gaithersburg, Md.] : , : U.S. Dept. of Commerce, Technology Administration, National Institute of Standards and Technology, , [2006] |
| Descrizione fisica | 1 online resource (175 unnumbered pages) : illustrations |
| Altri autori (Persone) |
ScarfoneKaren
SouppayaMurugiah ConnorJohn (Of Booz Allen Hamilton) |
| Collana | NIST special publication.Computer security |
| Soggetto topico |
Computer security - Standards
Microsoft software - Security measures |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Altri titoli varianti | Guidance for securing Microsoft Windows XP Home Edition |
| Record Nr. | UNINA-9910700820603321 |
| [Gaithersburg, Md.] : , : U.S. Dept. of Commerce, Technology Administration, National Institute of Standards and Technology, , [2006] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Guide to storage encryption technologies for end user devices (NIST special publication 800-111) : recommendations of the National Institute of Standards and Technology / / Karen Kent, Murugiah Souppaya, Matthew Sexton
| Guide to storage encryption technologies for end user devices (NIST special publication 800-111) : recommendations of the National Institute of Standards and Technology / / Karen Kent, Murugiah Souppaya, Matthew Sexton |
| Autore | Kent Karen (Karen Ann) |
| Edizione | [Draft.] |
| Pubbl/distr/stampa | Gaithersburg, Md. : , : U.S. Dept. of Commerce, , 2007 |
| Descrizione fisica | 1 online resource (40 pages) : illustrations |
| Disciplina | 005.8 |
| Collana | NIST special publication |
| Soggetto topico |
Computer networks - Security measures - United States
Computer security - Standards Data encryption (Computer science) |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Altri titoli varianti | Guide to Storage Encryption Technologies for End User Devices |
| Record Nr. | UNINA-9910698307703321 |
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Kent Karen (Karen Ann)
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| Gaithersburg, Md. : , : U.S. Dept. of Commerce, , 2007 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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