Intrusion detection for industrial control systems

<p>Industrial Control Systems (ICS) are rapidly shifting from closed local networks, to remotely accessible networks. This shift has created a need for strong cybersecurity anomaly and intrusion detection for these systems; however, due to the complexity and diversity of ICSs, well defined and reliable anomaly and intrusion detection systems are still being developed. Machine learning approaches for anomaly and intrusion detection on the network level may provide general protection that can be applied to any ICS. This paper explores two machine learning applications for classifying the attack label of the UNSW-NB15 dataset. The UNSW-NB15 is a benchmark dataset that was created off general network communications and includes labels for normal behavior and attack vectors. A baseline was created using K-Nearest Neighbors (kNN) due to its mathematical simplicity. Once the baseline was created a feed forward artificial neural network known as a Multi-Layer Perceptron (MLP), was implemented for comparison due to its ease of reuse for running in a production environment. The experimental results show that both kNN and MLPs are effective approaches for identifying malicious network traffic; although, both still need to be further refined and improved before implementation on a real-world production scale.</p>

Name thesis:degree_name

Master of Science (MS) in Computer Science

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Computer Science and Electrical Engineering

Year

2021

Author dc:creator

• Lamon, Kurt

• Computer Engineering
• Digital Communications and Networking
• Other Computer Engineering

Statement dc:rights

• Access is available to all users

Repository record dc:identifier

https://dc.ewu.edu/theses/662

OAI identifier oai:identifier

oai:dc.ewu.edu:theses-1662