In tema di obbligo di residenza del fallito : Note in margine ad una sentenza della Corte Costituzionale / MicheleScudiero

• Record Nr.: UNINA990006531360403321
• Autore: Scudiero, Michele
• Pubbl/distr/stampa: Napoli : SAV, s.d.
• Descrizione fisica: 12 p. ; 22 cm
• Disciplina: 343
• Locazione: FSPBC
• Collocazione: BUSTA I A 10; BUSTA I B 115
• Lingua di pubblicazione: Italiano
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: "Estratto dalla 'Rassegna di diritto pubblico'. 1962, fasc. II-III".

Trust and security in collaborative computing [[electronic resource] /] / Xukai Zou, Yuan-Shun Dai, Yi Pan

• Record Nr.: UNINA9910782274703321
• Autore: Zou Xukai <1963->
• Pubbl/distr/stampa: Hackensack, NJ, : World Scientific, c2008
• ISBN: 1-281-93358-9; 9786611933586; 981-279-088-8
• Descrizione fisica: 1 online resource (248 p.)
• Collana: Computer and network security ; ; v. 2
• Altri autori (Persone): DaiYuan-Shun; PanYi <1960->
• Disciplina: 005.8
• Soggetti: Computer security; Groupware (Computer software); Computer networks - Security measures
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Description based upon print version of record.
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Contents; Preface; Acknowledgments; List of Figures; List of Tables; 1. Introduction; 1.1 Overview of Trusted Collaborative Computing; 1.2 Basic Concepts in Terms of Security; 1.3 Basic Concepts in Terms of Reliability; 1.4 Abbreviations and Notations; 1.5 Outline; 2. Secure Group Communication (SGC); 2.1 Overview of Secure Group Communication (SGC); 2.2 Typical Group Key Management Schemes for SGC; 2.2.1 Centralized Group Key Distribution; 2.2.1.1 Key Tree (Logical Key Hierarchy); 2.2.1.2 Other Proposed Schemes; 2.2.2 De-centralized Group Key Management; 2.2.2.1 Iolus; 2.2.2.2 Other Proposed Schemes 2.2.3 (Distributed) Contributory Group Key Agreement; 2.2.3.1 Tree based Group Di.e-Hellman Key Agreement; 2.2.3.2 Other Proposed Schemes; 2.2.4 Distributed Group Key Distribution; 2.2.4.1 DGKD; 2.3 Enhanced Group Key Management for SGC; 2.3.1 SGC for Wireless and Mobile Ad Hoc Networks; 2.3.1.1 CRTDH; 2.3.1.2 Other Proposed Schemes; 2.3.2 Authenticated Key Exchange (AKE); 2.3.2.1 AUTH-CRTDH; 2.3.2.2 Other Proposed Schemes; 2.3.3 Self-Healing Key Distribution; 2.3.3.1 Self-Healing based on Polynomials and Secret Sharing; 2.3.3.2 Other Proposed ; Schemes; 2.3.4 Block-free Group Key Management 2.3.4.1 BF-TGDH; 2.3.5 Secure Dynamic Conferencing; 2.3.5.1 KTDCKM-SDC; 2.3.5.2 Other Proposed Schemes; 2.4 Conclusion; 3. Cryptography based Access Control; 3.1 Overview of Access Control in Collaborative Computing; 3.2 An Efficient Differential Access Control (DIF-AC) Scheme; 3.2.1 System Description and Initialization; 3.2.2 System Dynamics and Maintenance; 3.2.3 Discussion; 3.3 Cryptographic Hierarchical Access Control (CHAC) Schemes; 3.3.1 HACModel; 3.3.2 Directly Dependent Key Schemes; 3.3.3 Indirectly Dependent Key Schemes; 3.3.4 Polynomial and Interpolation based Schemes 3.3.5 An Efficient CHAC Scheme with Locality; 3.4 A Uniform CHAC Scheme Based on Access Polynomials; 3.4.1 Principle; 3.4.2 Key Computation/Derivation; 3.4.3 Node/Vertex Level Dynamics; 3.4.4 User Level Dynamics; 3.4.5 Security and Performance Analysis; 3.4.5.1 Security Analysis; 3.4.5.2 Performance Analysis; 3.4.6 An Illustrative Example and Experiment Results; 3.4.7 Discussion; 3.4.7.1 Enforcement of Other Access Models; 3.5 Conclusion; 4. Intrusion Detection and Defense; 4.1 Overview of Intrusion Detection and Defense; 4.2 Intruding Attacks; 4.3 Intrusion Detection Models 4.3.1 Anomaly Modeling; 4.3.2 Misuse Modeling; 4.3.3 Specification Modeling; 4.4 Intrusion Response; 4.5 DoS/DDoS Attacks ; 4.5.1 Typical DoS Attacks; 4.5.1.1 DoS Flooding Attacks; 4.5.1.2 Redirection Attacks; 4.5.1.3 Service Exploits; 4.5.2 Distributed Denial of Service (DDoS) Attacks; 4.5.2.1 DDoS Attack Steps; 4.5.2.2 DDoS Tools; 4.6 Typical DoS/DDoS Defense Mechanisms; 4.6.1 Single-node Defending Method; 4.6.2 Multiple-node Defending Methods; 4.6.2.1 Path Identification; 4.6.3 Honeypot; 4.7 Defending against DoS/DDoS Attacks-Traceback; 4.7.1 ICMP Traceback.; 4.7.2 (Probabilistic) IP Packet Marking
• Sommario/riassunto: Computer networks are compromised by various unpredictable factors, such as hackers, viruses, spam, faults, and system failures, hindering the full utilization of computer systems for collaborative computing - one of the objectives for the next generation of the Internet. It includes the functions of data communication, resource sharing, group cooperation, and task allocation. One popular example of collaborative computing is grid computing. This monograph considers the latest efforts to develop a trusted environment with the high security and reliability needed for collaborative computing.

Principles of Data Mining / / by Max Bramer

• Record Nr.: UNINA9910409668803321
• Autore: Bramer Max
• Pubbl/distr/stampa: London : , : Springer London : , : Imprint : Springer, , 2020
• ISBN: 1-4471-7493-3
• Edizione: [4th ed. 2020.]
• Descrizione fisica: 1 online resource (576 pages)
• Collana: Undergraduate Topics in Computer Science, , 1863-7310
• Disciplina: 006.312
• Soggetti: Information storage and retrieval; Database management; Artificial intelligence; Computer programming; Information Storage and Retrieval; Database Management; Artificial Intelligence; Programming Techniques
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Introduction to Data Mining -- Data for Data Mining -- Introduction to Classification: Naïve Bayes and Nearest Neighbour -- Using Decision Trees for Classification -- Decision Tree Induction: Using Entropy for Attribute Selection -- Decision Tree Induction: Using Frequency Tables for Attribute Selection -- Estimating the Predictive Accuracy of a Classifier -- Continuous Attributes -- Avoiding Overfitting of Decision Trees -- More About Entropy -- Inducing Modular Rules for Classification -- Measuring the Performance of a Classifier -- Dealing with Large Volumes of Data -- Ensemble Classification -- Comparing Classifiers -- Associate Rule Mining I -- Associate Rule Mining II -- Associate Rule Mining III -- Clustering -- Mining -- Classifying Streaming Data -- Classifying Streaming Data II: Time-dependent Data -- An Introduction to Neural Networks -- Appendix A – Essential Mathematics -- Appendix B – Datasets -- Appendix C – Sources of Further Information -- Appendix D – Glossary and Notation -- Appendix E – Solutions to Self-assessment Exercises -- Index.
• Sommario/riassunto: This book explains and explores the principal techniques of Data Mining, the automatic extraction of implicit and potentially useful information from data, which is increasingly used in commercial, scientific and other application areas. It focuses on classification, association rule mining and clustering. Each topic is clearly explained, with a focus on algorithms not mathematical formalism, and is illustrated by detailed worked examples. The book is written for readers without a strong background in mathematics or statistics and any formulae used are explained in detail. It can be used as a textbook to support courses at undergraduate or postgraduate levels in a wide range of subjects including Computer Science, Business Studies, Marketing, Artificial Intelligence, Bioinformatics and Forensic Science. As an aid to self-study, it aims to help general readers develop the necessary understanding of what is inside the 'black box' so they can use commercial data mining packages discriminatingly, as well as enabling advanced readers or academic researchers to understand or contribute to future technical advances in the field. Each chapter has practical exercises to enable readers to check their progress. A full glossary of technical terms used is included. Principles of Data Mining includes descriptions of algorithms for classifying streaming data, both stationary data, where the underlying model is fixed, and data that is time-dependent, where the underlying model changes from time to time - a phenomenon known as concept drift. The expanded fourth edition gives a detailed description of a feed-forward neural network with backpropagation and shows how it can be used for classification.