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.
|Title of host publication||Cyber Criminology|
|Place of Publication||Switzerland|
|Publisher||Springer International Publishing AG|
|Number of pages||20|
|Publication status||Published - 6 Dec 2018|
|Name||Advanced Sciences and Technologies for Security Applications|
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