Cybersecurity Attacks Analysis

Dataset Overview

Dataset Name

Cyber Security Attacks Analysis

Description

This dataset contains 25 different indicators and 40,000 records, intended to help cybersecurity professionals, researchers, and analysts understand trends and patterns of cybersecurity attacks.

Dataset Content

The dataset covers various indicators, including timestamp, source IP address, destination IP address, source port, destination port, protocol, packet length, packet type, traffic type, payload data, malware indicators, anomaly score, alerts/warnings, attack type, attack signature, actions taken, severity level, user information, device information, network segment, geographic location data, proxy information, firewall logs, IDS/IPS alerts and log sources.

Functionality

• Exploratory Data Analysis (EDA): In‑depth understanding of the dataset through summary statistics, unique‑value and missing‑value checks.
• Visualization: Create infographics such as state‑level attack distribution, severity‑level distribution, etc., using matplotlib and seaborn.
• Customization: Code can be easily adapted to different dataset structures and analysis goals.

Usage

1. Clone the repository.
2. Navigate to the project directory.
3. Open the Jupyter Notebook.
4. Explore the notebook to analyze and visualize the cybersecurity attack dataset.
5. Modify the code according to specific dataset columns and requirements to explore additional visualizations, statistical tests, or machine‑learning models.

Research Ideas

• EDA: Understand the context of each column and identify unique values, perform summary statistics and missing‑value checks.
• Visualization: Include attack‑type distribution, severity‑level distribution, correlation heatmap, and relationship between malware indicators and anomaly scores.
• State‑Level Analysis: Show attack distribution, severity‑level distribution, and attack‑type distribution across different states or regions.

Contribution

Contributions are welcome via forking the repository, making changes, and submitting pull requests to enhance analysis, add new visualizations, or fix issues.