Preprint Concept Paper Version 1 Preserved in Portico This version is not peer-reviewed

An Empirical Study of Deep Web based on Graph Analysis

Version 1 : Received: 4 November 2020 / Approved: 5 November 2020 / Online: 5 November 2020 (16:00:45 CET)

How to cite: Morshed, M.M. An Empirical Study of Deep Web based on Graph Analysis. Preprints 2020, 2020110219 (doi: 10.20944/preprints202011.0219.v1). Morshed, M.M. An Empirical Study of Deep Web based on Graph Analysis. Preprints 2020, 2020110219 (doi: 10.20944/preprints202011.0219.v1).

Abstract

The internet can broadly be divided into three parts: surface, deep and dark among which the latter offers anonymity to its users and hosts [1]. Deep Web refers to an encrypted network that is not detected on search engine like Google etc. Users must use Tor to visit sites on the dark web [2]. Ninety six percent of the web is considered as deep web because it is hidden. It is like an iceberg, in that, people can just see a small portion above the surface, while the largest part is hidden under the sea [3, 4, and 5]. Basic methods of graph theory and data mining, that deals with social networks analysis can be comprehensively used to understand and learn Deep Web and detect cyber threats [6]. Since the internet is rapidly evolving and it is nearly impossible to censor the deep web, there is a need to develop standard mechanism and tools to monitor it. In this proposed study, our focus will be to develop standard research mechanism to understand the Deep Web which will support the researchers, academicians and law enforcement agencies to strengthen the social stability and ensure peace locally & globally.

Subject Areas

dark web; cybercrime; law enforcement

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