Artificial intelligence tools for cyber attribution / Eric Nunes, Paulo Shakarian, Gerardo I. Simari, Andrew Ruef.
Nunes, Eric, author.; Shakarian, Paulo, author.; Simari, Gerardo I., author.; Ruef, Andrew, author.
2018
Q335
Available Online

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Title Artificial intelligence tools for cyber attribution / Eric Nunes, Paulo Shakarian, Gerardo I. Simari, Andrew Ruef.
ISBN 9783319737881 (electronic book)
3319737880 (electronic book)
9783319737874
3319737872
DOI https://doi.org/10.1007/978-3-319-73788-1
Published Cham, Switzerland : Springer, 2018.
Language English
Description 1 online resource (viii, 91 pages) : illustrations.
Item Number 10.1007/978-3-319-73788-1 doi
Call Number Q335
Dewey Decimal Classification 006.3
Summary 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.
Bibliography, etc. Note Includes bibliographical references.
Access Note Access limited to authorized users.
Digital File Characteristics text file PDF
Source of Description Online resource; title from PDF title page (SpringerLink, viewed February 20, 2018).
Added Author Nunes, Eric, author.
Shakarian, Paulo, author.
Simari, Gerardo I., author.
Ruef, Andrew, author.
Series SpringerBriefs in computer science.
Available in Other Form Print version: 9783319737874
Linked Resources Online Access
Record Appears in Online Resources > Ebooks
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Intro; Acknowledgements; Contents; 1 Introduction; References; 2 Baseline Cyber Attribution Models; 2.1 Introduction; 2.2 Dataset; 2.2.1 DEFCON CTF; 2.2.2 DEFCON CTF Data; 2.2.3 Analysis of CTF Data; 2.3 Baseline Approaches; 2.4 Experimental Results; 2.4.1 Misclassified Samples; 2.4.2 Pruning; 2.5 Conclusions; References; 3 Argumentation-Based Cyber Attribution: The DeLP3E Model; 3.1 Introduction; 3.1.1 Application to the Cyber Attribution Problem; 3.1.2 Structure of the Chapter; 3.2 Technical Preliminaries; 3.2.1 Basic Language; 3.2.2 Environmental Model; 3.2.3 Analytical Model

3.3 The DeLP3E Framework3.3.1 Warranting Scenarios; 3.3.2 Entailment in DeLP3E; 3.4 Consistency and Inconsistency in DeLP3E Programs; 3.5 Case Study: An Application in Cybersecurity; 3.5.1 Model for the Attribution Problem; 3.5.2 Applying Entailment to the Cyber Attribution Problem; 3.6 Conclusions; References; 4 Belief Revision in DeLP3E; 4.1 Introduction; 4.2 Basic Belief Revision; 4.2.1 EM-Based Belief Revision; 4.2.2 AM-Based Belief Revision; 4.2.2.1 Postulates for AM-Based Belief Revision; 4.2.2.2 AM-Based Revision Operators; 4.2.3 Annotation Function-Based Belief Revision

4.2.3.1 Postulates for Revising the Annotation Function4.2.3.2 AF-Based Revision Operators; 4.3 Quantitative Belief Revision Operators; 4.3.1 Towards Quantitative Revision; 4.3.2 Two Building Blocks; 4.3.3 The Class QAFO; 4.3.4 Computational Complexity; 4.3.5 Warranting Formulas; 4.3.6 Outlook: Towards Tractable Computations; 4.4 Conclusions and Future Work; References; 5 Applying Argumentation Models for Cyber Attribution; 5.1 Introduction; 5.2 Baseline Argumentation Model (BM); 5.3 Extended Baseline Model i (EB1); 5.4 Extended Baseline Model ii (EB2); 5.5 Conclusions; References

6 Enhanced Data Collection for Cyber Attribution6.1 Introduction; 6.2 Goals and Design; 6.2.1 Changing Contestant Behavior; 6.2.2 Game Rules; 6.2.3 Infrastructure Design; 6.2.4 Motivating Attribution and Deception; 6.2.5 Validity of Data; 6.3 Conclusion; References; 7 Conclusion

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