ReinforSec: An Automatic Generator of Synthetic Malware Samples and Denial-of-Service Attacks through Reinforcement...

Area of Science:

• Cybersecurity
• Machine Learning
• Data Science

Background:

• Cybersecurity relies on up-to-date threat intelligence, but acquiring realistic malware and Denial of Service (DoS) attack samples is challenging.
• Existing methods for obtaining attack data are often time-consuming, resource-intensive, and can lead to unbalanced or unrepresentative datasets.
• Synthetic data generation offers a potential solution to reduce costs and improve data diversity, but often lacks real-world applicability.

Purpose of the Study:

• To propose a methodology for generating synthetic samples of malicious Portable Executable (PE) binaries and DoS cyber-attacks.
• To address the limitations of current methods in acquiring fresh, representative, and functional attack data.
• To create adaptable datasets for enhancing machine learning-based cybersecurity defenses.

Main Methods:

• A Reinforcement Learning (RL) engine was employed to generate synthetic cyber-attack samples.
• The RL engine learned from a baseline of diverse malware families and DoS attack network properties.
• The methodology focuses on creating mutated, highly functional malicious binaries and DoS attack instances.

Main Results:

• The proposed methodology successfully generated novel, mutated, and functional synthetic samples of malware and DoS cyber-attacks.
• The generated synthetic samples demonstrated high adaptability as input datasets for various machine learning algorithms.
• Experimental results validated the effectiveness of the RL-driven approach in creating valuable cybersecurity data.

Conclusions:

• The RL-based synthetic sample generation methodology offers an efficient and effective solution for creating up-to-date cybersecurity threat data.
• This approach can significantly reduce the challenges associated with acquiring and preparing real-world malware and DoS attack datasets.
• The generated synthetic data enhances the training and performance of machine learning models for improved cyber-attack detection and defense.