Advertisement

Cost Optimized Random Sampling in Cellular Automata for Digital Forensic Investigations

  • Arnab Mitra
  • Anirban Kundu
Part of the Studies in Computational Intelligence book series (SCI, volume 555)

Abstract

In today’s world, advancement of Information Technology has been simultaneously followed by cyber crimes resulting in offensive and distressful digital contents. Threat to the digital content has initiated the need for application of forensic activities in digital field seeking evidence against any type of cyber crimes for the sake of reinforcement of the law and order. Digital Forensics is an interdisciplinary branch of computer science and forensic sciences, rapidly utilizing the recovery and/or investigation works on digital data explored in electronic memory based devices with reference to any cyber based unethical, illegal, and unauthorized activities. A typical digital forensic investigation work follows three steps to collect evidence(s): content acquisition, content analysis and report generation. In digital content analysis higher amount of data volumes and human resource(s) exposure to distressing and offensive materials are of major concerns. Lack of technological support for processing large amount of offensive data makes the analytical procedure quite time consuming and expensive. Thus, it results in a degradation of mental health of concerned investigators. Backlog in processing time by law enforcement department and financial limitations initiate huge demand for digital forensic investigators turning out trustworthy results within reasonable time. Forensic analysis is performed on randomly populated sample, instead of entire population size, for faster and reliable analysis procedure of digital contents. Present work reports about an efficient design methodology to facilitate random sampling procedure to be used in digital forensic investigations. Cellular Automata (CA) based approach has been used in our random sampling method. Equal Length Cellular Automata (ELCA) based pseudo-random pattern generator (PRPG) has been proposed in a cost effective manner utilizing the concept of random pattern generator. Exhibition of high degree randomness has been demonstrated in the field of randomness quality testing. Concerned cost effectiveness refers to time complexity, space complexity, design complexity and searching complexity. This research includes the comparative study for some well known random number generators, e.g., recursive pseudo-random number generator (RPRNG), atmospheric noise based true-random number generator (TRNG), Monte-Carlo (M-C) pseudo-random number generator, Maximum Length Cellular Automata (MaxCA) random number generator and proposed Equal Length Cellular Automata (ELCA) random number generator.Resulting sequences for all those above mentioned pattern generatorshave significant improvement in terms of randomness quality. Associated fault coverage is being improved using iterative methods. Emphasis on cost effectiveness has been initiated for proposed random sampling in forensic analysis.

Keywords

Digital Forensic Investigation Random sampling Recursive pseudo- random number generator (RPRNG) True-random number generator (TRNG) Monte-Carlo (M-C) pseudo-random number generator Cellular Automata (CA) Maximum Length Cellular Automata (MaxCA) Equal Length Cellular Automata (ELCA) 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Casey, E.: Digital Evidence and Computer Crime, 2nd edn. Elsevier (2004)Google Scholar
  2. 2.
  3. 3.
    Jones, B., Pleno, S., Wilkinson, M.: The use of random sampling in investigations involving child abuse material. Digital Investigation 9 (2012), http://www.dfrws.org/2012/proceedings/DFRWS2012-11.pdf
  4. 4.
    Wolfram, S.: Wolfram Mathematica Tutorial Collection: Random Number Generation, http://mathworld.wolfram.com/RandomNumber.html
  5. 5.
    Eddelbuettel, D.: Random: An R package for true random numbers, http://dirk.eddelbuettel.com/bio/papers.html
  6. 6.
  7. 7.
    Zio, E., Podofillini, L., Zille, V.: A combination of Monte Carlo simulation and cellular automata for computing the availability of complex network systems. Reliability Engineering System Staff (2006)Google Scholar
  8. 8.
    Gurov, T., Ivanovska, S., Karaivanova, A., Manev, N.: Monte Carlo Methods Using New Class of Congruential Generators. In: Kocarev, L. (ed.) ICT Innovations 2011. AISC, vol. 150, pp. 257–267. Springer, Heidelberg (2012)Google Scholar
  9. 9.
  10. 10.
    True Random Numbers, http://www.random.org/
  11. 11.
    Wolfram, S.: Theory and Application of Cellular Automata. World Scientific (1986)Google Scholar
  12. 12.
    Chaudhuri, P.P., Chowdhury, D.R., Nandi, S., Chattopadhyay, S.: Additive Cellular Automata Theory and Applications, vol. 1. IEEE Computer Society Press (1997)Google Scholar
  13. 13.
    Das, S., Kundu, A., Sikdar, B.K.: Nonlinear CA Based Design of Test Set Generator Targeting Pseudo-Random Pattern Resistant Faults. In: Asian Test Symposium, Taiwan (2004)Google Scholar
  14. 14.
    Das, S., Sikdar, B.K., Chaudhuri, P.P.: Nonlinear CA Based Scalable Design of On-Chip TPG for Multiple Cores. In: Asian Test Symposium, Taiwan (2004)Google Scholar
  15. 15.
    Martinez, D.G., Doinguez, A.P.: Pseudorandom number generation based on Nongroup Cellular Automata. In: IEEE 33rd Annual International Carnahan Conference on Security Technology (1999)Google Scholar
  16. 16.
    Das, S., Rahaman, H., Sikdar, B.K.: Cost Optimal Design of Nonlinear CA Based PRPG for Test Applications. In: IEEE 14th Asian Test Symposium, India (2005)Google Scholar
  17. 17.
    Hortensius, P.D., Pries, W., Card, H.C.: Cellular Automata based Pseudorandom Number generators for Built-In Self-Test. IEEE Transactions on Computer-Aided Design 8(8) (1989)Google Scholar
  18. 18.
    Bardell, P.H.: Analysis of Cellular Automata Used as Pseudorandom pattern Generators. In: International Test Conference (1990)Google Scholar
  19. 19.
    Das, S., Kundu, A., Sikdar, B.K., Chaudhuri, P.P.: Design of Nonlinear CA Based TPG Without Prohibited Pattern Set In Linear Time. Journal of Electrical Testing Theory and Applications (2005)Google Scholar
  20. 20.
    Das, S., Kundu, A., Sen, S., Sikdar, B.K., Chaudhuri, P.P.: Non-Linear Celluar Automata Based PRPG Design (Without Prohibited Pattern Set) in Linear Time Complexity. In: Asian Test Symposium, Chaina (2003)Google Scholar
  21. 21.
    Ganguly, N., Nandi, A., Das, S., Sikdar, B.K., Chaudhuri, P.P.: An Evolutionary Strategy To Design An On-Chip Test Pattern Generator Without Prohibited Pattern Set (PPS). In: Asian Test Symposium, Guam (2002)Google Scholar
  22. 22.
    Brown, R.G.: Dieharder: A Random Number Test Suite, C program archive dieharder, version1.4.24 (2006a), http://www.phy.duke.edu/~rgb/General/dieharder.php
  23. 23.
    Mora, R., Kloet, B.: The Application of statistical sampling in Digital forensics. Hoffmann Investigations, Almere The Netherlands (2010), https://blogs.sans.org/computer-forensics/files/2010/03/statisticalforensictriage.pdf
  24. 24.
    Vel, O.D., Liu, N., Caelli, T., Caetano, T.S.: An Embedded Bayesian Network Hidden Markov Model for Digital Forensics. In: 4th IEEE International Conference on Intelligence and Security Informatics, USA (2006), doi: 10.1007/11760146_41Google Scholar
  25. 25.
    Mitra, A., Kundu, A.: Cost Optimized Approach to Random Numbers in Cellular Automata. In: Wyld, D.C., Zizka, J., Nagamalai, D. (eds.) Advances in Computer Science, Engineering & Applications. AISC, vol. 166, pp. 609–618. Springer, Heidelberg (2012)Google Scholar
  26. 26.
    Mitra, A., Kundu, A.: Cellular Automata based Cost Optimized PRNG for Monte-Carlo Simulation in Distributed Computing. In: CUBE International Information Technology Conference & Exhibition 2012, India (2012)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Adamas Institute of TechnologyWest BengalIndia
  2. 2.Kuang-Chi Institute of Advanced TechnologyShenzhenP.R. China
  3. 3.Innovation Research Lab (IRL)West BengalIndia

Personalised recommendations