Cyber Incident Intelligence Platform

🛡️ A cybersecurity analytics platform for threat intelligence and risk prediction

Overview

The Cyber Incident Intelligence Platform is an end-to-end analytics and machine learning system that transforms raw cybersecurity incident data into actionable intelligence.

It enables security analysts and decision-makers to understand attack patterns, measure organizational risk exposure, and predict the likelihood of high-severity breaches using statistical analysis and machine learning.

It performs:

• Exploratory data analysis (EDA)
• Statistical testing
• Feature engineering
• Predictive modeling (logistic regression)
• Interactive visualization via a Streamlit dashboard

The goal is to transform raw breach data into actionable security intelligence.

Business Impact

The Cyber Incident Intelligence Platform helps organizations:

• Identify high-risk sectors vulnerable to cyberattacks
• Understand how attack patterns evolve over time
• Quantify data exposure from cybersecurity incidents
• Predict breach severity using machine learning
• Support proactive cybersecurity decision-making

This transforms raw data into actionable security intelligence

Key Questions

• Threat landscape

  • What attack types are increasing over time?
  • Which industries experience the most incidents?
  • Which attack methods are most damaging?

• Risk exposure

  • Which sectors lose the most records?
  • Which attack vectors cause the largest breaches?

• Predictive insights

  • Can we predict breach severity based on incident attributes?

• Strategic intelligence

  • What trends should organizations prepare for?

Tech Stack

• Python (pandas, numpy, scipy)
• Machine Learning (scikit-learn)
• Visualization (matplotlib, seaborn)
• Dashboard (Streamlit)
• Configuration (YAML)

Pipeline

1. Data pipeline (src/data/data_pipeline.py)

   • Load raw CSV or generate sample data (generate_data.py)
   • Clean data, handle missing values, remove duplicates
   • Save processed dataset

2. Feature engineering (src/features/feature_engineering.py)

   • breach_severity
   • incident_year
   • records_lost_log
   • industry_risk_score

3. Exploratory analysis (src/analysis/exploratory_analysis.py)

   • Attack distribution
   • Sector incident counts
   • Yearly trends
   • Correlation matrix

4. Statistical tests (src/statistics/statistical_tests.py)

   • Chi-square test: attack type vs sector
   • Pearson correlation tests

5. Modeling (src/models/risk_prediction.py)

   • Logistic regression to predict high-severity breaches

6. Visualization (src/visualization/visualization.py)

   • Attack type distribution
   • Incidents over time
   • Sector risk comparison
   • Correlation heatmap

7. Dashboard (app/dashboard.py)

   • Streamlit app for interactive exploration

System Architecture

Raw Data → Cleaning → Feature Engineering → ML Model → Dashboard

A modular pipeline that transforms raw cybersecurity data into predictive intelligence through preprocessing, feature engineering, and machine learning.

Dashboard Preview

Installation

git clone https://github.com/ysf-sheikh/cyber-incident-intelligence-platform.git cyber-incident-intelligence
cd cyber-incident-intelligence
python -m venv venv
source venv/bin/activate  # Windows: venv\Scripts\activate
pip install -r requirements.txt
pip install -e .

Usage

Optional: Generate sample data

If you don’t have a real dataset, you can generate a mock dataset using:

python src/data/generate_data.py

The generated dataset includes:

• incident_date – date of the incident (2020–2024)
• sector – e.g., Finance, Healthcare, Retail, Government, Education, Technology
• attack_type – e.g., Ransomware, Phishing, SQL Injection, DDoS, Insider Threat, Malware
• records_lost – number of records compromised (follows a skewed distribution; Finance incidents tend to have higher losses, and recent incidents favor Ransomware/Phishing)

1. Run the Data Pipeline

Process the raw data and generate engineered features:

python main.py

2. Launch the Intelligence Dashboard

Start the interactive Streamlit interface:

streamlit run app/dashboard.py