Cybersecurity has become one of the most critical challenges of the digital age. With the exponential growth of data and digital systems, incidents involving data breaches, unauthorized access, and digital fraud have risen sharply. This project presents an in-depth visual and statistical analysis of cyber crime incidents reported between 2019 and 2024, aiming to uncover key patterns, trends, and insights that can inform policy decisions and risk mitigation strategies.
As cyber threats continue to evolve, understanding their nature, frequency, and impact has become essential for organizations and governments alike. The absence of timely insights into cyber crime trends can lead to poor incident response, underprepared sectors, and increased vulnerability of sensitive data.
Goal:
To analyze cyber crime incident data across multiple dimensions and answer key questions such as:
- Which sectors are most frequently targeted?
- What are the most common types of incidents?
- How has the number of affected data subjects changed over time?
- What actions are typically taken following an incident?
- What is the ratio of cyber to non-cyber incidents?
This analysis helps in identifying weak links, improving cyber resilience, and allocating resources effectively.
The dataset comprises detailed incident records from 2019 to 2024, including:
- Sectors impacted (e.g., Healthcare, Finance, Government)
- Incident types (Phishing, Unauthorized Access, Ransomware, etc.)
- Number of affected data subjects
- Response actions taken (e.g., Informal Action, Investigation, No Further Action)
- Categorization into Cyber and Non-Cyber incidents
- Temporal breakdown by year and quarter
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📌 Distribution of Incidents by Sector
Identifies high-risk sectors with the greatest incident frequency. -
📌 Decision Taken Post Incident
Analyzes the frequency of responses such as investigations, informal actions, or inaction. -
📌 Density Distribution of Affected Subjects
Visualizes the spread and skewness in the number of individuals affected per incident. -
📌 Cyber vs Non-Cyber Incident Breakdown
Offers a comparative view using a clear and concise pie chart. -
📌 Year-wise Affected Subject Distribution
Tracks fluctuations in impact across the years using violin plots. -
📌 Top 5 Incident Types
Highlights the most common threats across all sectors. -
📌 Incident Category by Sector
Cross-analyzes incident type distribution within each sector. -
📌 Quarterly Trend of Average Affected Subjects
Reveals temporal patterns and peak periods of cyber impact.
- Python – Data processing and analysis
- Pandas & NumPy – Data manipulation
- Matplotlib & Seaborn – Interactive and static data visualizations
- VS CODE – For exploratory data analysis and visualization
- Healthcare, Finance, and Government sectors are consistently the most targeted.
- A significant number of incidents result in no further action, indicating possible gaps in enforcement or policy.
- Phishing, Unauthorised Access, and Misdelivery are among the most frequent cyber threats.
- The average number of affected individuals shows seasonal spikes and sudden dips, reflecting changing threat vectors and detection/reporting behavior.
- Non-Cyber incidents still outnumber cyber incidents but cyber threats are growing steadily.
- Integrate with real-time data streams for live dashboards.
- Use machine learning to forecast high-risk periods and sectors.
- Expand with geo-location analysis for regional threat visualization.