Use of Hardware Fingerprinting for Intrusion Detection in Avionics Systems

Date

2025-6

Author

Babir, İsa Can

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

60
views

0
downloads

Cite This

With the emergence of next-generation avionic platforms, systems that were previously isolated from external networks are now exposed to attacks. Traditional aviation communication buses and avionics systems, such as the MIL-STD-1553 standard, were developed without considering security requirements, as external attacks were deemed improbable due to their closed architecture. MIL-STD-1553, widely implemented across commercial, military, and aerospace avionic platforms, is a communication bus where security concerns were largely overlooked. However, this notion of invulnerability has since been disproven. As aircraft systems become more interconnected with external networks, they are increasingly vulnerable to attacks that threaten operational effectiveness. Implementing security measures, which require hardware and software modifications, results in costly and complex certification challenges, particularly for legacy systems. Intrusion Detection Systems (IDS) have gained popularity as a solution, as they do not require hardware or software modifications. This study aims to enhance the security of MIL-STD-1553 communication by integrating a Hardware Fingerprinting-Based IDS. The research evaluates the effectiveness of machine and deep learning techniques in detecting unauthorized devices on the bus. Supervised learning methods achieved perfect classification accuracy, excelling in precision, recall, and F1-score, while unsupervised methods showed limited success in anomaly detection. Additionally, a feature reduction process was applied to improve performance, revealing that supervised methods maintained high accuracy with fewer features, while unsupervised methods saw performance decline. Lastly, the study investigates the stability of synchronization signals over time, finding they remain consistent, supporting the reliability of the device’s unique characteristics.

Subject Keywords

MIL-STD-1553, Intrusion detection, Hardware fingerprinting, Machine and deep learning

URI

https://hdl.handle.net/11511/115066

Collections

Graduate School of Informatics, Thesis