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Applying Reinforcement Learning for Enhanced Cybersecurity against Adversarial Simulation.

Sang Ho Oh¹, Min Ki Jeong², Hyung Chan Kim³

¹Business Department of Convergence and Open Sharing System, Seoul National University of Science and Technology, Seoul 01811, Republic of Korea.

Sensors (Basel, Switzerland)

|March 30, 2023

Summary

This summary is machine-generated.

Deep reinforcement learning (DRL) enhances cybersecurity by simulating adversarial cyber-attacks. This agent-based model learns optimal attack strategies in dynamic network environments, outperforming traditional methods.

Keywords:

adversarial simulation artificial intelligence cybersecurity deep reinforcement learning

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Area of Science:

Computer Science
Artificial Intelligence
Cybersecurity

Background:

Traditional cybersecurity methods struggle against advanced threats.
Reinforcement learning (RL) shows promise but faces data and modeling challenges.
Effective RL applications in cybersecurity require advanced frameworks.

Purpose of the Study:

To apply a deep reinforcement learning (DRL) framework for adversarial cyber-attack simulation.
To enhance cybersecurity defenses through adaptive learning.
To address limitations in current RL cyber applications.

Main Methods:

Developed an agent-based model utilizing deep reinforcement learning (DRL).
Simulated adversarial cyber-attacks in a dynamic network environment.
Agent learns optimal attack actions based on network state and rewards.

Main Results:

The DRL framework demonstrated superior performance in learning optimal attack actions.
The agent-based model adapted effectively to the dynamic network security environment.
Outperformed existing methods in adversarial cyber-attack simulation.

Conclusions:

DRL offers a powerful approach to developing dynamic cybersecurity solutions.
The proposed framework advances the state of the art in RL for cyber applications.
Promising step towards more resilient and adaptive cybersecurity.