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.
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Mitra, A., Kundu, A. (2014). Cost Optimized Random Sampling in Cellular Automata for Digital Forensic Investigations. In: Muda, A., Choo, YH., Abraham, A., N. Srihari, S. (eds) Computational Intelligence in Digital Forensics: Forensic Investigation and Applications. Studies in Computational Intelligence, vol 555. Springer, Cham. https://doi.org/10.1007/978-3-319-05885-6_5
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DOI: https://doi.org/10.1007/978-3-319-05885-6_5
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