Please use this identifier to cite or link to this item:
https://repository.iimb.ac.in/handle/2074/11782
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Murthy, A S N | |
dc.contributor.author | Nagadevara, Vishnuprasad | |
dc.contributor.author | De, Rahul | |
dc.date.accessioned | 2020-04-22T13:50:26Z | - |
dc.date.available | 2020-04-22T13:50:26Z | - |
dc.date.issued | 2010 | |
dc.identifier.issn | 1935-5688 | |
dc.identifier.uri | https://repository.iimb.ac.in/handle/2074/11782 | - |
dc.description.abstract | With increased access to computers across the world, cybercrime is becoming a major challenge to law enforcement agencies. Cybercrime investigation in India is in its infancy and there has been limited success in prosecuting the offenders; therefore, a need to understand and strengthen the existing investigation methods and systems for controlling cybercrimes is greatly needed. This study identifies important factors that will enable law enforcement agencies to reach the first step in effective prosecution, namely charge-sheeting of the cybercrime cases. Data on 300 cybercrime cases covering a number of demographic, technical and other variables related to cybercrime was analyzed using data mining techniques to identify and prioritize various factors leading to filing of the charge-sheet. These factors and the respective priority rankings are used to suggest various policy measures for improving the success rate of prosecution of cybercrimes. | |
dc.publisher | IGI Global Publishing | |
dc.subject | Charge-Sheeting | |
dc.subject | Classification Trees | |
dc.subject | Cybercrime | |
dc.subject | Data Mining | |
dc.subject | Neural Nets | |
dc.title | Predictive models in cybercrime investigation: an application of data mining techniques | |
dc.type | Journal Article | |
dc.identifier.doi | 10.4018/JISSS.2010070101 | |
dc.pages | 1-12p. | |
dc.vol.no | Vol.2 | - |
dc.issue.no | Iss.3 | - |
dc.journal.name | International Journal of information Systems in The Service Sector | |
Appears in Collections: | 2010-2019 |
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