Crime data mining, threat analysis and prediction

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

Research output: Chapter in Book/Report/Conference proceedingChapter


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.
Original languageEnglish
Title of host publicationCyber Criminology
PublisherSpringer Nature
Publication statusPublished - 12 Jun 2018


Dive into the research topics of 'Crime data mining, threat analysis and prediction'. Together they form a unique fingerprint.

Cite this