This project analyzes global cybersecurity incidents to identify patterns in cyber attacks, financial losses, targeted industries, and threat actors. The analysis combines SQL, Python, and Power BI to explore the dataset, perform data analysis, and create an interactive cyber threat dashboard. The goal of this project is to demonstrate how data analytics can be applied to cybersecurity data to uncover insights about global cyber threats.

The dataset contains global cybersecurity incidents between 2015 and 2024.

Source: https://www.kaggle.com/datasets/atharvasoundankar/global-cybersecurity-threats-2015-2024

Each record includes:

• Country
• Year
• Attack Type
• Target Industry
• Financial Loss (in Million $)
• Number of Affected Users
• Attack Source
• Security Vulnerability Type
• Defense Mechanism Used
• Incident Resolution Time

The dataset contains 3000 cybersecurity incidents across multiple industries and countries.

This project uses multiple tools to simulate a real-world data analysis workflow.

• SQL (SQLite)

• Python
• Pandas
• Matplotlib
• Seaborn

• Power BI

CSV Dataset → SQLite Database → SQL Analysis → Python Visualization → Power BI Dashboard

global-cyber-threat-analysis
│
├── data
│   └── Global_Cybersecurity_Threats_2015-2024.csv
│
├── sql
│   └── analysis.sql
│
├── python
│   ├── import_data.py
│   ├── visualization.py
│   └── visualization_attacksourcedist.py
│
├── dashboard
│   └── cyber-threats.pbix
│
├── requirements.txt
├── README.md
└── LICENSE

SQL was used to explore and analyze the cybersecurity dataset stored in SQLite.

SELECT *
FROM threats
LIMIT 10;

SELECT "Attack Type", COUNT(*) as attacks
FROM threats
GROUP BY "Attack Type"
ORDER BY attacks DESC;

SELECT Country,
SUM("Financial Loss (in Million $)") as losses
FROM threats
GROUP BY Country
ORDER BY losses DESC;

SELECT "Attack Type",
SUM("Financial Loss (in Million $)") as total_loss
FROM threats
GROUP BY "Attack Type"
ORDER BY total_loss DESC;

SELECT "Attack Source",
COUNT(*) as incidents
FROM threats
GROUP BY "Attack Source"
ORDER BY incidents DESC;

SELECT "Target Industry",
COUNT(*) as attacks
FROM threats
GROUP BY "Target Industry"
ORDER BY attacks DESC;

Shows which industries are most frequently targeted.

SELECT Year,
COUNT(*) as incidents
FROM threats
GROUP BY Year
ORDER BY Year;

Analyzes how the number of cyber attacks changes over time.

SELECT Country,
Year,
"Attack Type",
"Target Industry",
"Financial Loss (in Million $)"
FROM threats
ORDER BY "Financial Loss (in Million $)" DESC
LIMIT 10;

1. Clone the repository

git clone https://github.com/yourusername/global-cyber-threat-analysis.git

2. Import the dataset into SQLite

python import_data.py

This script loads the CSV dataset into a SQLite database (cyber_threats.db).

3. Run SQL analysis queries

Open the database using the SQLite extension in VS Code and run queries from:

analysis.sql

4. Run Python visualizations

python visualization.py

5. Open the Power BI dashboard

Open the file:

cyber-threats.pbix

to explore the interactive cybersecurity dashboard.

• DDoS and phishing attacks are the most frequent cyber threats.
• DDoS attacks generate the highest financial losses among the analyzed attack types.
• Nation-state actors and unknown attackers are responsible for the largest share of cyber incidents.
• IT, banking, and healthcare sectors are among the most frequently targeted industries.
• Cyber incidents remain consistently high across the years analyzed (2015–2024).
• Large-scale cyber attacks can result in financial losses approaching 100 million USD per incident.

• Add machine learning models to predict cyber attack trends
• Integrate threat intelligence APIs