TENSOR: a solution to dark web investigations

DAY, Tony, DENNIS, Kieran and GIBSON, Helen (2021). TENSOR: a solution to dark web investigations. In: AKHGAR, Babak, GERCKE, Marco, VROCHIDIS, Stefanos and GIBSON, Helen, (eds.) Dark Web Investigation. Security Informatics and Law Enforcement . Springer, 119-135.

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Abstract

A wide range of criminal activities, particularly those related to hate crime, radicalisation and terrorism, have an ever-increasing online component that needs to be considered, captured and investigated. Activity on the Dark Web is becoming more commonplace and can be a significant factor in investigations. Such information may need to be coupled with other open-source information from the surface web and social media. Due to this, law enforcement is undergoing radical changes in their investigatory processes requiring new digital systems; however, aspects of the Dark Web still remain ‘off limits’ to investigators as they battle technical, regulatory and organisational challenges. TENSOR is conceived as a system that can address many of these challenges by providing a mechanism for investigators to capture intelligence from online sources including the Dark Web and convert it into structured and coherent data. Within the system, TENSOR implements a unique data model, acquisition and extraction process that enables information to become searchable, analysable and relatable. Several analytical capabilities are layered above delivering advanced functionalities including natural language processing and concept extraction, machine translation, image recognition and iconography detection, social network analysis, stylometry, content similarity recommendations and image forensics. Each capability is presented through a unified intelligence dashboard that provides a clear entry point and logical analysis pathway for investigation to ensure TENSOR delivers a system that enables law enforcement to access and analyse complex information from the surface, Deep and Dark Web.

Item Type:	Book Section
Uncontrolled Keywords:	Technology & Engineering
Identification Number:	https://doi.org/10.1007/978-3-030-55343-2_5
Page Range:	119-135
SWORD Depositor:	Symplectic Elements
Depositing User:	Symplectic Elements
Date Deposited:	05 Sep 2023 11:16
Last Modified:	11 Oct 2023 12:01
URI:	https://shura.shu.ac.uk/id/eprint/32131

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