Machine Learning in Cyber Trust Security, Privacy, and Reliability

Machine Learning in Cyber Trust Security, Privacy, and Reliability / edited by Jeffrey J. P. Tsai, Philip S. Yu.

Many networked computer systems are far too vulnerable to cyber attacks that can inhibit their functioning, corrupt important data, or expose private information. Not surprisingly, the field of cyber-based systems turns out to be a fertile ground where many tasks can be formulated as learning proble...

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Bibliographic Details
Corporate Author: SpringerLink (Online service)
Other Authors: Tsai, Jeffrey J. P. (Editor), Yu, Philip S. (Editor)
Format: eBook
Language:English
Published: New York, NY : Springer US : Imprint: Springer, 2009.
Edition:1st ed. 2009.
Series:Springer eBook Collection.
Subjects:
Computer security.
Data mining.
Artificial intelligence.
Electronic resources (E-books)
Online Access:Click to view e-book
Holy Cross Note:Loaded electronically.
Electronic access restricted to members of the Holy Cross Community.
Table of Contents:
  • Cyber System
  • Cyber-Physical Systems: A New Frontier
  • Security
  • Misleading Learners: Co-opting Your Spam Filter
  • Survey of Machine Learning Methods for Database Security
  • Identifying Threats Using Graph-based Anomaly Detection
  • On the Performance of Online Learning Methods for Detecting Malicious Executables
  • Efficient Mining and Detection of Sequential Intrusion Patterns for Network Intrusion Detection Systems
  • A Non-Intrusive Approach to Enhance Legacy Embedded Control Systems with Cyber Protection Features
  • Image Encryption and Chaotic Cellular Neural Network
  • Privacy
  • From Data Privacy to Location Privacy
  • Privacy Preserving Nearest Neighbor Search
  • Reliability
  • High-Confidence Compositional Reliability Assessment of SOA-Based Systems Using Machine Learning Techniques
  • Model, Properties, and Applications of Context-Aware Web Services.

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