Authors: Hassan Chizari; Shukor Abd Razak; Mojib Majidi; Shaharuddin Bin Salleh
Addresses: Department of Computer Science, Faculty of Computing, Universiti Teknologi Malaysia, Malaysia ' Department of Computer Science, Faculty of Computing, Universiti Teknologi Malaysia, Malaysia ' Department of Computer Science, Faculty of Computing, Universiti Teknologi Malaysia, Malaysia ' Department of Mathematics, Faculty of Science, Universiti Teknologi Malaysia, Malaysia
Abstract: File type identification (FTI) is the problem of determining the file type from its content. FTI, as a computer forensic challenge, has been studied extensively with many solutions provided by researchers. One of the most popular methodologies to do so is the mathematical analysis, which examines the distribution of bytes to explore the file type [byte frequency distribution (BFD) equations]. The main question, which is left behind, is that how one can generalise his or her proposed FTI algorithm to all files? In this work, firstly, a normality assessment test has been applied for various BFD's equations, which showed none of the BFD's histogram is normal distribution. Then, using Renkonen correlation to compare non-normal distributions, the proper sample sizes, which is population representative, were presented based upon the file type and BFD's equations. Finally, it has been shown that using bootstrap method the BFD's distribution can be converted into a normal distribution.
Keywords: file type identification; FTI; sample size; non-normal distribution; byte frequency distribution; BFD.
International Journal of Advanced Intelligence Paradigms, 2018 Vol.11 No.1/2, pp.58 - 74
Available online: 04 Jul 2018Full-text access for editors Access for subscribers Purchase this article Comment on this article