Network Simulation with Complex Cyber-attack Scenarios

TL;DR

This paper introduces a network simulation framework integrated into Airbus CyberRange to generate realistic datasets with complex cyber-attack scenarios for training and evaluating Network Intrusion Detection systems.

Contribution

It presents a novel simulation approach for creating complex attack scenarios within a realistic network topology, enhancing NID dataset quality.

Findings

Simulated MitM, DoS, and BF attack scenarios in CyberRange.

Generated datasets improve NID training with realistic traffic.

Enhanced simulation supports complex attack interactions.

Abstract

Network Intrusion Detection (NID) systems can benefit from Machine Learning (ML) models to detect complex cyber-attacks. However, to train them with a great amount of high-quality data, it is necessary to perform reliable simulations of multiple interacting machines. This paper presents a network simulation solution for the creation of NID datasets with complex attack scenarios. This solution was integrated in the Airbus CyberRange platform to benefit from its simulation capabilities of generating benign and malicious traffic patterns that represent realistic cyber-attacks targeting a computer network. A realistic vulnerable network topology was configured in the CyberRange and three different attack scenarios were implemented: Man-in-the-Middle (MitM), Denial-of-Service (DoS), and Brute-Force (BF).