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Using Natural Language Processing and Machine Learning to Detect Online Grooming Attacks

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Advances in Computational Intelligence Systems (UKCI 2022)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1454))

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Abstract

A Natural Language Processing solution which incorporates Online Grooming phases has been developed within this paper. This solution coded each phrase within a transcript between a Honeypot profile and an Online Groomer on a chatroom to one of these phases. This was then compared to a human reviewed coding of each of these phrases to check for accuracy. The paper found that this coding identified the Initiation phase (with underaged declaration detection) within 75% of the transcripts with a 3% false positive rate. Most detections were incorrect for the Risk Assessment and Sexual phases. From analysis of this some words/phrase used in the Sexual phase detection had significantly more ‘incorrect’ human reviews than ‘correct’ (21%). It is likely that through filtration of these words/phrases an effective solution could be established, as 38% of these words/phrases had significantly more ‘correct’ human reviews than ‘incorrect’.

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Author information

Authors and Affiliations

  1. Nottingham Trent University, Nottingham, NG1 4FQ, UK

    Jake Street & Funminiyi Olajide

Authors
  1. Jake Street
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  2. Funminiyi Olajide
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Corresponding author

Correspondence to Jake Street .

Editor information

Editors and Affiliations

  1. Department of Automatic Control and Systems Engineering, The University of Sheffield, Sheffield, UK

    George Panoutsos

  2. Department of Automatic Control and Systems Engineering, Head of the Intelligent Systems Research Laboratory, The University of Sheffield, Sheffield, South Yorkshire, UK

    Mahdi Mahfouf

  3. Department of Automatic Control and Systems Engineering, The University of Sheffield, Sheffield, South Yorkshire, UK

    Lyudmila S Mihaylova

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Street, J., Olajide, F. (2024). Using Natural Language Processing and Machine Learning to Detect Online Grooming Attacks. In: Panoutsos, G., Mahfouf, M., Mihaylova, L.S. (eds) Advances in Computational Intelligence Systems. UKCI 2022. Advances in Intelligent Systems and Computing, vol 1454. Springer, Cham. https://doi.org/10.1007/978-3-031-55568-8_22

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