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This project employs ensemble learning methods to forecast cybercrime rates, utilizing datasets with population, internet subscriptions, and crime incidents. By analyzing trends and employing metrics like R2 Score and Mean Squared Error, it aims to enhance prediction accuracy and provide insights for effective prevention strategies.

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RohithAthalury/Forecasting-and-Analysis-of-Cyber-Crimes

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Forecasting and Analysis of Cyber Crimes

Introduction:

Cybercrime is a growing concern globally, with its impacts extending across individuals, businesses, and governments. To address this challenge effectively, accurate forecasting and analysis of cybercrime trends are essential. In this project, we focus on leveraging ensemble learning techniques to forecast cybercrime rates and analyze various cybercrime categories. Our approach involves a comparative analysis of several ensemble learning algorithms, namely Bagging, Gradient Boosting, Ada Boosting, Random Forest, and XG Boost. We evaluate these models using metrics such as R2 Score, Mean Squared Error (MSE), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE). Additionally, we provide predicted crime rates based on input parameters including Year, State, Population, Total Broadband Subscriptions, Total Internet Subscriptions, and Total Wireless Internet Subscriptions.

Datasets Used:

Main Dataset: This dataset comprises attributes such as unique code, state name, year, population, total internet subscriptions, total broadband subscriptions, total wireless subscriptions, and reported cybercrime cases. It serves as the primary data source for predicting cybercrime rates. Cybercrime Categories Dataset: This dataset includes attributes such as unique code, state name, year, population, total internet subscriptions, total broadband subscriptions, total wireless subscriptions, and specific cybercrime categories like identity theft, cyberstalking, online banking fraud, etc. It facilitates in-depth analysis of various cybercrime types. Yearly Crime Trends Dataset: This dataset contains state-wise reported crime cases for the years 2015 to 2020. It aids in identifying patterns and trends in cybercrime over consecutive years.

Methodology:

Data Preprocessing:

We perform data cleaning, handling missing values, and feature engineering to prepare the datasets for analysis.

Ensemble Learning Models:

We implement Bagging, Gradient Boosting, Ada Boosting, Random Forest, and XG Boost algorithms to develop predictive models for cybercrime rates. Each model is trained, validated, and optimized using appropriate techniques.

Comparative Analysis:

We evaluate the performance of each model using metrics such as R2 Score, MSE, MAE, and RMSE. This comparative analysis helps identify the most effective ensemble learning technique for cybercrime rate prediction.

Crime Category Analysis:

Utilizing the Cybercrime Categories Dataset, we analyze the prevalence and trends of specific cybercrime types across different states and years.

Trend Identification:

Leveraging the Yearly Crime Trends Dataset, we identify significant patterns and trends in cybercrime rates over the years, enabling insights into evolving cyber threats.

Results and Interpretation:

The results of our analysis provide valuable insights into the effectiveness of ensemble learning techniques for cybercrime forecasting. We identify the top-performing model based on the evaluation metrics and provide interpretations of the predicted crime rates. Additionally, our analysis of cybercrime categories and yearly trends offers actionable insights for stakeholders in cybersecurity and law enforcement.

Conclusion:

This project demonstrates the utility of ensemble learning techniques in forecasting cybercrime rates and analyzing trends in cybercrime categories. By employing sophisticated machine learning algorithms and comprehensive datasets, we contribute to enhancing understanding and preparedness in combating cyber threats. Our findings can inform proactive measures and policy interventions aimed at mitigating cyber risks and safeguarding digital ecosystems.

Future Directions:

Future research can explore advanced ensemble learning methods, incorporate additional data sources, and delve deeper into the socio-economic factors influencing cybercrime dynamics. Moreover, ongoing monitoring and analysis of cybercrime trends are crucial for adapting strategies and interventions in response to evolving threats.

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This project employs ensemble learning methods to forecast cybercrime rates, utilizing datasets with population, internet subscriptions, and crime incidents. By analyzing trends and employing metrics like R2 Score and Mean Squared Error, it aims to enhance prediction accuracy and provide insights for effective prevention strategies.

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