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Policy-Based Autonomic Data Governance

Overview of attention for book
Cover of 'Policy-Based Autonomic Data Governance'

Table of Contents

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    Book Overview
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    Chapter 1 AGENP: An ASGrammar-based GENerative Policy Framework
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    Chapter 2 Value of Information: Quantification and Application to Coalition Machine Learning
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    Chapter 3 Self-Generating Policies for Machine Learning in Coalition Environments
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    Chapter 4 Can N-Version Decision-Making Prevent the Rebirth of HAL 9000 in Military Camo? Using a “Golden Rule” Threshold to Prevent AI Mission Individuation
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    Chapter 5 Simulating User Activity for Assessing Effect of Sampling on DB Activity Monitoring Anomaly Detection
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    Chapter 6 FADa-CPS—Faults and Attacks Discrimination in Cyber Physical Systems
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    Chapter 7 Techniques and Systems for Anomaly Detection in Database Systems
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    Chapter 8 Towards Enabling Trusted Artificial Intelligence via Blockchain
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    Chapter 9 Secure Model Fusion for Distributed Learning Using Partial Homomorphic Encryption
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    Chapter 10 Policy-Based Identification of IoT Devices’ Vendor and Type by DNS Traffic Analysis
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    Chapter 11 Redundancy as a Measure of Fault-Tolerance for the Internet of Things: A Review
Attention for Chapter 7: Techniques and Systems for Anomaly Detection in Database Systems
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Citations

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Chapter title
Techniques and Systems for Anomaly Detection in Database Systems
Chapter number 7
Book title
Policy-Based Autonomic Data Governance
Published by
Springer, Cham, January 2019
DOI 10.1007/978-3-030-17277-0_7
Book ISBNs
978-3-03-017276-3, 978-3-03-017277-0
Authors

Asmaa Sallam, Elisa Bertino, Sallam, Asmaa, Bertino, Elisa

View on publisher site
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 4 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 4 100%

Demographic breakdown

Readers by professional status Count As %
Unspecified 1 25%
Researcher 1 25%
Unknown 2 50%
Readers by discipline Count As %
Unspecified 1 25%
Agricultural and Biological Sciences 1 25%
Unknown 2 50%

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