Crime data mining, threat analysis and prediction

Farsi, M., Daneshkhah, A., Hosseinian-Far, A., Chatrabgoun, O. and Montasari, R. (2018) Crime data mining, threat analysis and prediction. In: Jahankhani, H. (ed.) Cyber Criminology. Springer. (Accepted)

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Item Type:	Book Section
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 Internetconnectivity, 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.
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science > QA76.9.A25 Computer security
Creators:	Farsi, Maryam, Daneshkhah, Alireza, Hosseinian-Far, Amin, Chatrabgoun, Omid and Montasari, Reza
Editors:	Jahankhani, Hamid
Publisher:	Springer
Faculties, Divisions and Institutes:	University Faculties, Divisions and Research Centres - OLD > Faculty of Business & Law > Business University Faculties, Divisions and Research Centres - OLD > Faculty of Business & Law > Law Faculties > Faculty of Business & Law > Law Faculties > Faculty of Business & Law > International Strategy & Business
Date:	12 June 2018
Date Type:	Acceptance
Title of Book:	Cyber Criminology
Language:	English
Status:	Accepted
Refereed:	Yes
URI:	http://nectar.northampton.ac.uk/id/eprint/10402

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