Machine Learning in Cyber Trust

Security, Privacy, and Reliability

  • Philip S. Yu
  • Jeffrey J. P.  Tsai

Table of contents

  1. Front Matter
    Pages 1-13
  2. Cyber System

    1. Front Matter
      Pages 1-1
    2. Lui Sha, Sathish Gopalakrishnan, Xue Liu, Qixin Wang
      Pages 3-13
  3. Security

    1. Front Matter
      Pages 15-15
    2. Blaine Nelson, Marco Barreno, Fuching Jack Chi, Anthony D. Joseph, Benjamin I. P. Rubinstein, Udam Saini et al.
      Pages 17-51
    3. Ashish Kamra, Elisa Ber
      Pages 53-71
    4. William Eberle, Lawrence Holder, Diane Cook
      Pages 73-108
    5. Shangping Ren, Nianen Chen, Yue Yu, Pierre Poirot, Kevin Kwiat, Jeffrey J.P. Tsai
      Pages 155-181
    6. Jun Peng, Du Zhang
      Pages 183-213
  4. Privacy

    1. Front Matter
      Pages 215-215
    2. Ting Wang, Ling Liu
      Pages 217-246
    3. Mark Shaneck, Yongdae Kim, Vipin Kumar
      Pages 247-276
  5. Reliability

    1. Front Matter
      Pages 277-277
    2. Venkata U. B. Challagulla, Farokh B. Bastani, I-Ling Yen
      Pages 279-322
    3. Stephen J. H. Yang, Jia Zhang, Angus F. M. Huang
      Pages 323-358
  6. Back Matter
    Pages 359-362

About this book

Introduction

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 problems and approached in terms of machine learning algorithms.

This book contains original materials by leading researchers in the area and covers applications of different machine learning methods in the security, privacy, and reliability issues of cyber space. It enables readers to discover what types of learning methods are at their disposal, summarizing the state of the practice in this important area, and giving a classification of existing work.

Specific features include the following:

  • A survey of various approaches using machine learning/data mining techniques to enhance the traditional security mechanisms of databases
  • A discussion of detection of SQL Injection attacks and anomaly detection for defending against insider threats
  • An approach to detecting anomalies in a graph-based representation of the data collected during the monitoring of cyber and other infrastructures
  • An empirical study of seven online-learning methods on the task of detecting malicious executables
  • A novel network intrusion detection framework for mining and detecting sequential intrusion patterns
  • A solution for extending the capabilities of existing systems while simultaneously maintaining the stability of the current systems
  • An image encryption algorithm based on a chaotic cellular neural network to deal with information security and assurance
  • An overview of data privacy research, examining the achievements, challenges and opportunities while pinpointing individual research efforts on the grand map of data privacy protection
  • An algorithm based on secure multiparty computation primitives to compute the nearest neighbors of records in horizontally distributed data
  • An approach for assessing the reliability of SOA-based systems using AI reasoning techniques
  • The models, properties, and applications of context-aware Web services, including an ontology-based context model to enable formal description and acquisition of contextual information pertaining to service requestors and services

Those working in the field of cyber-based systems, including industrial managers, researchers, engineers, and graduate and senior undergraduate students will find this an indispensable guide in creating systems resistant to and tolerant of cyber attacks.

Keywords

Spam Web security classification control cyber terrorism intrusion detection learning learning algorithms machine learning performance privacy reliability security

Editors and affiliations

  • Philip S. Yu
  • Jeffrey J. P.  Tsai

There are no affiliations available

Bibliographic information

  • DOI https://doi.org/10.1007/978-0-387-88735-7
  • Copyright Information Springer-Verlag US 2009
  • Publisher Name Springer, Boston, MA
  • eBook Packages Computer Science
  • Print ISBN 978-0-387-88734-0
  • Online ISBN 978-0-387-88735-7
  • About this book