Crime Data Mining, Threat Analysis and Prediction

Maryam Farsi, Alireza Daneshkhah, Amin Hosseinian Far, Omid Chatrabgoun, Reza Montasari

Research output: Chapter in Book/Report/Conference proceedingChapter

4 Citations (Scopus)

Abstract

Cybercriminology as a subject area has numerous dimensions. Some studies in the field primarily focus on a corrective action to reduce the impact of an already committed crime. However, there are existing computational techniques which can assist in predicting and therefore preventing cyber-crimes. These quantitative techniques are capable of providing valuable holistic and strategic insights for law enforcement units and police forces to prevent the crimes from happening. Moreover, these techniques can be used to analyse crime patterns to provide a better understanding of the world of cyber-criminals. The main beneficiaries of such research works, are not only the law enforcement units, as in the era of Internet-connectivity, many business would also benefit from cyber attacks and crimes being committed in the cyber environment. This chapter provides an all-embracing overview of machine learning techniques for crime analysis followed by a detailed critical discussion of data mining and predictive analysis techniques within the context of cybercriminology.
Original languageEnglish
Title of host publicationCyber Criminology
EditorsHamid Jahankhani
Place of PublicationSwitzerland
PublisherSpringer International Publishing AG
Chapter9
Pages183-202
Number of pages20
Edition1st
ISBN (Electronic)9783319971810
ISBN (Print)9783319971803
DOIs
Publication statusPublished - 6 Dec 2018

Publication series

NameAdvanced Sciences and Technologies for Security Applications
ISSN (Print)1613-5113

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  • Cite this

    Farsi, M., Daneshkhah, A., Hosseinian Far, A., Chatrabgoun, O., & Montasari, R. (2018). Crime Data Mining, Threat Analysis and Prediction. In H. Jahankhani (Ed.), Cyber Criminology (1st ed., pp. 183-202). (Advanced Sciences and Technologies for Security Applications). Springer International Publishing AG. https://doi.org/10.1007/978-3-319-97181-0_9