Ransomware Dataset 2024

The dataset we have created is focused on malware analysis and consists of 26 different malware families, categorized into four main categories. It includes both malicious and benign samples, providing a balanced total of 21,752 samples, with 10,876 malicious and 10,876 benign files.

Key Aspects of the Dataset:

1. Total Samples: 21,752 files (10,876 malicious and 10,876 benign).

2. Malware Families: The dataset contains 26 distinct malware families, with a strong focus on ransomware, which includes:

   • Cerber
   • DarkSide
   • Dharma
   • GandCrab
   • LockBit
   • Maze
   • Phobos
   • REvil
   • Ragnar Locker
   • Ryuk
   • Shade
   • WannaCry

   These 11 ransomware families represent some of the most notorious strains responsible for large-scale attacks in recent years.

3. Categories: The dataset is divided into four categories, although you haven’t specified the exact categorization scheme (it could be based on behavior, type of attack, or other malware features). Typical categories could include Trojan, Ransomware, Spyware, and Adware.

Significance of the Dataset:

• Balanced Distribution: The dataset is evenly distributed between malicious and benign files, making it ideal for machine learning models that can differentiate between malware and benign software.
• Ransomware Focus: By including major ransomware families, this dataset allows for specialized research in ransomware detection, mitigation, and family classification.
• Diversity in Malware Types: The inclusion of 26 malware families ensures a wide spectrum of malware behavior and characteristics, making the dataset versatile for research in various malware categories.

Applications:

• Machine Learning and AI: This dataset can be used to train models for malware classification, detection, and family identification.
• Cybersecurity Research: It supports analysis and countermeasure development against ransomware and other forms of malware.
• Forensic Analysis: Researchers can use it to investigate attack patterns, signature generation, and the impact of ransomware on different systems.

This dataset is valuable for advancing malware analysis, specifically in understanding ransomware behavior, and for building robust defenses against increasingly sophisticated attacks.