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1. |
Record Nr. |
UNINA9910144117003321 |
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Autore |
Gurtov Andrei |
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Titolo |
Host Identity Protocol (HIP) : towards the secure mobile Internet / / Andrei Gurtov |
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Pubbl/distr/stampa |
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Chichester, U.K. : , : Wiley, , 2008 |
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[Piscataqay, New Jersey] : , : IEEE Xplore, , [2008] |
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ISBN |
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1-281-84107-2 |
9786611841072 |
0-470-77289-1 |
0-470-77290-5 |
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Descrizione fisica |
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1 online resource (323 p.) |
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Collana |
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Wiley series on communications networking & distributed systems |
Wiley series in communications networking & distributed systems |
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Disciplina |
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Soggetti |
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Wireless Internet - Security measures |
Host Identity Protocol (Computer network protocol) |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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Foreword / Jari Arkko -- Foreword / David Hutchison -- Introduction to network security -- Architectural overview -- Base protocol -- Main extensions -- Advanced extensions -- Performance measurements -- Lightweight HIP -- Infrastructure support -- Middlebox traversal -- Name resolution -- Micromobility -- Communication privacy -- Possible HIP applications -- Application interface -- Integrating HIP with other protocols -- Installing and using HIP. |
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Sommario/riassunto |
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"Within the set of many identifier-locator separation designs for the Internet, HIP has progressed further than anything else we have so far. It is time to see what HIP can do in larger scale in the real world. In order to make that happen, the world needs a HIP book, and now we have it." - Jari Arkko, Internet Area Director, IETF One of the challenges facing the current Internet architecture is the incorporation of mobile and multi-homed terminals (hosts), and an overall lack of protection against Denial-of-Service attacks and identity spoofing. The Host Identity Protocol (HI |
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2. |
Record Nr. |
UNINA9910847583403321 |
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Autore |
Guerraoui Rachid |
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Titolo |
Robust Machine Learning : Distributed Methods for Safe AI / / by Rachid Guerraoui, Nirupam Gupta, Rafael Pinot |
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Pubbl/distr/stampa |
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Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024 |
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ISBN |
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Edizione |
[1st ed. 2024.] |
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Descrizione fisica |
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1 online resource (0 pages) |
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Collana |
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Machine Learning: Foundations, Methodologies, and Applications, , 2730-9916 |
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Disciplina |
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Soggetti |
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Machine learning |
Computer security |
Multiagent systems |
Cloud computing |
Machine Learning |
Principles and Models of Security |
Multiagent Systems |
Cloud Computing |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di bibliografia |
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Includes bibliographical references. |
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Nota di contenuto |
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Chapter 1. Context & Motivation -- Chapter 2. Basics of Machine Learning -- Chapter 3. Federated Machine Learning -- Chapter 4. Fundamentals of Robust Machine Learning -- Chapter 5. Optimal Robustness -- Chapter 6. Practical Robustness. . |
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Sommario/riassunto |
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Today, machine learning algorithms are often distributed across multiple machines to leverage more computing power and more data. However, the use of a distributed framework entails a variety of security threats. In particular, some of the machines may misbehave and jeopardize the learning procedure. This could, for example, result from hardware and software bugs, data poisoning or a malicious player controlling a subset of the machines. This book explains in simple terms what it means for a distributed machine learning scheme to be robust to these threats, and how to build provably robust machine learning algorithms. Studying the robustness of machine learning algorithms is a necessity given the ubiquity of these algorithms in both |
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the private and public sectors. Accordingly, over the past few years, we have witnessed a rapid growth in the number of articles published on the robustness of distributed machine learning algorithms. We believe it is time to provide a clear foundation to this emerging and dynamic field. By gathering the existing knowledge and democratizing the concept of robustness, the book provides the basis for a new generation of reliable and safe machine learning schemes. In addition to introducing the problem of robustness in modern machine learning algorithms, the book will equip readers with essential skills for designing distributed learning algorithms with enhanced robustness. Moreover, the book provides a foundation for future research in this area. . |
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