2018
DOI: 10.1002/spy2.40
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Significant data region identification and analysis using k‐means in large storage drive forensics

Abstract: The recent technological advancement with less implementation cost has completely changed the scenario of information being generated, stored, or manipulated digitally by using the storage devices. Today the criminal activities are directly or indirectly associated with the storage devices, thus it is now recognized as an essential evidence in court-of-law throughout the world. However, the massive capacities of modern storage devices significantly increase the time and effort required to preserve and analyze … Show more

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Cited by 2 publications
(2 citation statements)
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“…Randomly-selected sectors are easily classified as null or non-null sectors based on their content [4]. In this work, the entropy metric is used to identify sectors with human-readable, multimedia (images, audio and video), encoded, compressed, encrypted or executable content.…”
Section: Extracted Featuresmentioning
confidence: 99%
“…Randomly-selected sectors are easily classified as null or non-null sectors based on their content [4]. In this work, the entropy metric is used to identify sectors with human-readable, multimedia (images, audio and video), encoded, compressed, encrypted or executable content.…”
Section: Extracted Featuresmentioning
confidence: 99%
“…Their approach was to divide the drive into the equal sized region and evaluate a specified number of samples independently from each region for detecting the desired artifacts, without employing structural information of the drive. In another work, Bharadwaj and Singh provided a method to segregate the significant and nonsignifcant regions of the drive using random sector sampling method and a simple two cluster K‐means algorithm. Their method also provide the resident data pattern of the drive on the basis of the feature metrics related to the randomly selected null and non‐null sectors.…”
Section: Background and Related Workmentioning
confidence: 99%