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Predictive Models in Cybercrime Investigation: An Application of Data Mining Techniques

机译:网络犯罪调查中的预测模型:数据挖掘技术的应用

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摘要

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.
机译:随着全球计算机访问量的增加,网络犯罪正成为执法机构的主要挑战。印度的网络犯罪调查尚处于起步阶段,起诉罪犯的成功有限;因此,非常需要理解和加强用于控制网络犯罪的现有调查方法和系统。这项研究确定了重要因素,这些因素将使执法机构能够采取有效措施的第一步,即对网络犯罪案件进行收费。使用数据挖掘技术分析了涉及网络犯罪的大量人口,技术和其他变量的300例网络犯罪案件的数据,以识别和优先考虑导致收费表归档的各种因素。这些因素和相应的优先级等级用于建议各种政策措施,以提高起诉网络犯罪的成功率。

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