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Artificial Intelligence Tools for Cyber Attribution

  • Eric Nunes
  • Paulo Shakarian
  • Gerardo I. Simari
  • Andrew Ruef

Part of the SpringerBriefs in Computer Science book series (BRIEFSCOMPUTER)

Table of contents

  1. Front Matter
    Pages i-viii
  2. Eric Nunes, Paulo Shakarian, Gerardo I. Simari, Andrew Ruef
    Pages 1-3
  3. Eric Nunes, Paulo Shakarian, Gerardo I. Simari, Andrew Ruef
    Pages 5-16
  4. Eric Nunes, Paulo Shakarian, Gerardo I. Simari, Andrew Ruef
    Pages 17-45
  5. Eric Nunes, Paulo Shakarian, Gerardo I. Simari, Andrew Ruef
    Pages 47-74
  6. Eric Nunes, Paulo Shakarian, Gerardo I. Simari, Andrew Ruef
    Pages 75-84
  7. Eric Nunes, Paulo Shakarian, Gerardo I. Simari, Andrew Ruef
    Pages 85-90
  8. Eric Nunes, Paulo Shakarian, Gerardo I. Simari, Andrew Ruef
    Pages 91-91

About this book

Introduction

This SpringerBrief discusses how to develop intelligent systems for cyber attribution regarding cyber-attacks. Specifically, the authors review the multiple facets of the cyber attribution problem that make it difficult for “out-of-the-box” artificial intelligence and machine learning techniques to handle.

 Attributing a cyber-operation through the use of multiple pieces of technical evidence (i.e., malware reverse-engineering and source tracking) and conventional intelligence sources (i.e., human or signals intelligence) is a difficult problem not only due to the effort required to obtain evidence, but the ease with which an adversary can plant false evidence.

This SpringerBrief not only lays out the theoretical foundations for how to handle the unique aspects of cyber attribution – and how to update models used for this purpose – but it also describes a series of empirical results, as well as compares results of specially-designed frameworks for cyber attribution to standard machine learning approaches.

 Cyber attribution is not only a challenging problem, but there are also problems in performing such research, particularly in obtaining relevant data. This SpringerBrief describes how to use capture-the-flag for such research, and describes issues from organizing such data to running your own capture-the-flag specifically designed for cyber attribution. Datasets and software are also available on the companion website.

Keywords

Cyber security Cyber attribution Cyber attacks Artificial intelligence Argumentation Machine Learning Capture-The-Flag Games Defeasible Logic Programming Reasoning Framework

Authors and affiliations

  • Eric Nunes
    • 1
  • Paulo Shakarian
    • 2
  • Gerardo I. Simari
    • 3
  • Andrew Ruef
    • 4
  1. 1.Arizona State UniversityTempeUSA
  2. 2.Arizona State UniversityTempeUSA
  3. 3.Department of Computer Science and EngineeringUniversidad Nacional del Sur (UNS) & Institute for Computer Science and Engineering (UNS-CONICET)Bahia BlancaArgentina
  4. 4.University of MarylandCollege ParkUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-73788-1
  • Copyright Information The Author(s) 2018
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science
  • Print ISBN 978-3-319-73787-4
  • Online ISBN 978-3-319-73788-1
  • Series Print ISSN 2191-5768
  • Series Online ISSN 2191-5776
  • Buy this book on publisher's site