Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Selection and Evolutionary Development of Software-Service Bundles: A Capability Based Method Read chapter Read first chapter

2016 | OriginalPaper | Chapter

File Type Identification for Digital Forensics

Authors : Konstantinos Karampidis, Giorgios Papadourakis

Published in: Advanced Information Systems Engineering Workshops

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

In modern world the use of digital devices for leisure or professional reasons (computers, tablets and smartphones etc.) is growing quickly. Nevertheless, criminals try to fool authorities and hide evidence in a computer or any other digital device, by changing the file type. File type detection is a very demanding task for a digital forensic examiner. In this paper a new methodology is proposed – in a digital forensics perspective- to identify altered file types with high accuracy by employing computational intelligence techniques. The proposed methodology is applied in the four most common types of files (jpg, png and gif). A three stage process involving feature extraction (Byte Frequency Distribution), feature selection (genetic algorithm) and classification (neural network) is proposed. Experimental results were conducted having files altered in a digital forensics perspective and the results are presented. The proposed model shows very high and exceptional accuracy in file type identification.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Extending HARM to make Test Cases for Penetration Testing
Literature
1.
Palmer, G.: A road map for digital forensic research. In: Proceedings of the 2001 Digital Forensic Research Workshop (DFRWS 2004), pp. 1–42 (2001)
2.
Meghanathan, N., Boumerdassi, S., Chaki, N., Nagamalai, D. (eds.): Recent Trends in Network Security and Applications. Springer, Heidelberg (2010)
3.
4.
McDaniel, M.: Automatic File Type Detection Algorithm (2001)
5.
McDaniel, M., Heydari, M.H.: Content based file type detection algorithms. In: 2003 Proceedings of the 36th Annual Hawaii International Conference System Sciences (2003)
6.
Li, W.J., Wang, K., Stolfo, S.J., Herzog, B.: Fileprints: identifying file types by n-gram analysis. In: Proceedings from the 6th Annual IEEE Systems, Man, and Cybernetics Information Assurance Workshop SMC 2005, pp. 64–71 (2005)
7.
Dunham, J., Sun, M., Tseng, J.: Classifying file type of stream ciphers in depth using neural networks. In: The 3rd ACS/IEEE International Conference on Computer Systems and Applications (2005)
8.
Amirani, M.C., Toorani, M., Shirazi, A.A.B.: A new approach to content-based file type detection. In: Proceedings of the IEEE Symposium on Computers and Communications, pp. 1103–1108 (2008)
9.
Cao, D., Luo, J., Yin, M., Yang, H.: Feature selection based file type identification algorithm. In: 2010 IEEE International Conference on Intelligent Computing and Intelligent Systems, pp. 58–62. IEEE (2010)
10.
Ahmed, I., Lhee, K., Shin, H., Hong, M.: Content-based file-type identification using cosine similarity and a divide-and-conquer approach. IETE Tech. Rev. 27, 465 (2010)CrossRef
11.
Ahmed, I., Lhee, K.-S., Shin, H.-J., Hong, M.-P.: Fast content-based file type identification. In: Peterson, G., Shenoi, S. (eds.) Advances in Digital Forensics VII. IFIP AICT, vol. 361, pp. 65–75. Springer, Heidelberg (2015)CrossRef
12.
Amirani, M.C., Toorani, M., Mihandoost, S.: Feature-based type identification of file fragments. Secur. Commun. Netw. 6, 115–128 (2013)CrossRef
13.
14.
Karampidis, K., Papadourakis, G., Deligiannis, I.: File type identification – a literature review. In: Proceedings of 9th International Conference on New Horizons in Industry Business and Education, NHIBE 2015, p. 141, Skiathos, Greece (2015)
15.
Vafaie, H., De Jong, K.: Genetic algorithms as a tool for feature selection in machine learning. In: International Conference on Tools with AI, pp. 200–203 (1992)
16.
Zhuo, L., Zheng, J., Wang, F., Li, X., Ai, B., Qian, J.: A genetic algorithm based wrapper feature selection method for classification of hyper spectral data using support vector machine. Geogr. Res. 27, 493–501 (2008)
17.
Jourdan, L., Dhaenens, C., Talbi, E.: A genetic algorithm for feature selection in data-mining for genetics. In: Proceedings of the 4th Metaheuristics International Conference (2001)
18.
19.
Harris, R.: Using artificial neural networks for forensic file type identification. Master’s thesis, Purdue University (2007)
20.
Kohavi, R.: A study of cross-validation and bootstrap for accuracy estimation and model selection. In: 14th International Joint Conference on Artificial Intelligence, pp. 1137–1143 (1995)
21.
Fei-Fei, L., Fergus, R., Perona, P.: Learning generative visual models from few training examples: an incremental Bayesian approach tested on 101 object categories. In: Proceedings of Conference on Computer Vision and Pattern Recognition (CVPR), Workshop on Generative Model Based Vision 2004, p.178 (2004)
22.
23.
24.
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software. ACM SIGKDD Explor. Newsl. 11, 10 (2009)CrossRef
25.
Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley Publishing Company, Boston (1989)
Metadata
Title
File Type Identification for Digital Forensics
Authors
Konstantinos Karampidis
Giorgios Papadourakis
Copyright Year
2016
Book
Advanced Information Systems Engineering Workshops

Print ISBN: 978-3-319-39563-0

Electronic ISBN: 978-3-319-39564-7

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-39564-7_25

Premium Partner

    Image Credits