short-paper

Sustainable automated data recovery: a research roadmap

Author:

Jeroen van den BosAuthors Info & Claims

SERF 2017: Proceedings of the 1st ACM SIGSOFT International Workshop on Software Engineering and Digital Forensics

Pages 6 - 9

https://doi.org/10.1145/3121252.3121254

Published: 04 September 2017 Publication History

Abstract

Digital devices contain increasingly more data and applications. This means more data to handle and a larger amount of different types of traces to recover and consider in digital forensic investigations. Both present a challenge to data recovery approaches, requiring higher performance and increased flexibility.

In order to progress to a long-term sustainable approach to automated data recovery, this paper proposes a partitioning into manual, custom, formalized and self-improving approaches. These approaches are described along with research directions to consider: building universal abstractions, selecting appropriate techniques and developing user-friendly tools.

References

[1]

Matej Balog, Alexander L. Gaunt, Marc Brockschmidt, Sebastian Nowozin, and Daniel Tarlow. 2016.

[2]

DeepCoder: Learning to Write Programs. CoRR abs/1611.01989 (2016).

[3]

Jeroen van den Bos and Tijs van der Storm. 2011.

[4]

Bringing Domain-Specific Languages to Digital Forensics. In Proceedings of the 33rd International Conference on Software Engineering (ICSE’11). ACM, 671–680.

[5]

Brian Carrier. 2005.

[6]

File System Forensic Analysis. Addison-Wesley.

[7]

Eoghan Casey (Ed.). 2009.

[8]

Handbook of Digital Forensics and Investigation. Academic Press.

[9]

Geoffroy Couprie. 2015.

[10]

Nom, A Byte oriented, streaming, Zero copy, Parser Combinators Library in Rust. In Security and Privacy Workshops (SPW), 2015 IEEE.

[11]

IEEE, 142–148.

[12]

Kathleen Fisher, Yitzhak Mandelbaum, and David Walker. 2010. The Next 700 Data Description Languages. J. ACM 57, 2 (2010), 10:1–10:51.

Digital Library

[13]

Simson L. Garfinkel. 2013. Digital Media Triage with Bulk Data Analysis and bulk_extractor. Computers & Security 32, 0 (2013), 56–72.

[14]

Trevor Jim, Yitzhak Mandelbaum, and David Walker. 2010. Semantics and Algorithms for Data-Dependent Grammars. In ACM Sigplan Notices, Vol. 45. ACM, 417–430.

Digital Library

[15]

Falcon Momot, Sergey Bratus, Sven M. Hallberg, and Meredith L. Patterson. 2016.

[16]

The Seven Turrets of Babel: A Taxonomy of LangSec Errors and How to Expunge Them. In Proceedings of IEEE Cybersecurity Development (SecDev’16). IEEE, 45–52.

[17]

Anindrabatha Pal and Nasir Memon. 2009. The Evolution of File Carving. Signal Processing Magazine 26, 2 (2009), 59–71.

[18]

Emilio Parisotto, Abdel-rahman Mohamed, Rishabh Singh, Lihong Li, Dengyong Zhou, and Pushmeet Kohli. 2016.

[19]

Neuro-Symbolic Program Synthesis. CoRR abs/1611.01855 (2016).

[20]

Phillip D. Summers. 1977. A Methodology for LISP Program Construction from Examples. Journal of the ACM (JACM) 24, 1 (1977), 161–175.

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Index Terms

Sustainable automated data recovery: a research roadmap
1. Applied computing
  1. Computer forensics
    1. Data recovery
    2. Evidence collection, storage and analysis
2. Software and its engineering
  1. Software creation and management
    1. Software development techniques
      1. Automatic programming
  2. Software organization and properties
    1. Software system structures
      1. Software system models
        Model-driven software engineering

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cover image ACM Conferences

SERF 2017: Proceedings of the 1st ACM SIGSOFT International Workshop on Software Engineering and Digital Forensics

September 2017

11 pages

ISBN:9781450351560

DOI:10.1145/3121252

General Chairs:
Dalal Alrajeh,
Liliana Pasquale

Copyright © 2017 Owner/Author.

This work is licensed under a Creative Commons Attribution International 4.0 License.

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SIGSOFT: ACM Special Interest Group on Software Engineering

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Association for Computing Machinery

New York, NY, United States

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Published: 04 September 2017

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ESEC/FSE'17

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SIGSOFT

ESEC/FSE'17: Joint Meeting of the European Software Engineering Conference and the ACM SIGSOFT Symposium on the Foundations of Software Engineering

September 4, 2017

Paderborn, Germany

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