International Journal of Science, Engineering and TechnologyAuthors: Manisha M. More, Mayuri More, Varsha Gidde, Sheetal Sandeep Patil
Abstract: The proposed study explores the NCRB i.e. National Crime Record Bureau data for the period of 2012 To 2022 and assessed the trends cybercrime across the states, the metropolitan cities, and the sectors such as IT and IPC. The analysis is conducted using Power BI for data visualization and statistical methods in Python looking for any trends or correlations. In addition, clustering techniques were utilized in grouping states and sector data observation into three groups, i.e., low risk, medium risk and high risk. The analysis showed that most states were classified as low risk and only a few instead showed an extremely high intensity of Cyber Crime suggesting a degree of geographical concentration for Cyber Crime. The authors also used two types of machine learning model (Linear Regression and Random Forest) to identify any trends based on time. However, both machine learning models displayed limited success in their predictability meaning that Cyber Crime occurrences cannot simply be explained by reference to time alone. The overall findings of this study then align with the conclusion that while some Cyber Crime occurrences are similar and concentrated to a particular location or point in time, they are also dissimilar based upon when they occur and where they occur. As a whole the study illustrates a methodical analytical technique of using both data visualization through statistical methods and explorative AI methods to provide better insight into Cyber Crime trends.
DOI: http://doi.org/10.5281/zenodo.21700719
