Publikation
Keep Private Networks Private: Secure Channel-PUFs, and Physical Layer Security by Linear Regression Enhanced Channel Profiles
Christoph Lipps; Sachinkumar Bavikatti Malikarjun; Mathias Strufe; Christopher Heinz; Christoph Grimm; Hans Dieter Schotten
In: Proceedings of the 3rd International Conference on Data Intelligence and Security. International Conference on Data Intelligence and Security (ISDIS-2020), November 10-12, South Padre Island, Texas, USA, Pages 93-100, ISBN 978-1-7281-9379-3/20, IEEEXplore, 11/2020.
Zusammenfassung
In the context of a rapidly changing and increasingly
complex (industrial) production landscape, securing the (commu-
nication) infrastructure is becoming an ever more important but
also more challenging task - accompanied by the application of
radio communication. A worthwhile and promising approach to
overcome the arising attack vectors, and to keep private networks
private, are Physical Layer Security (PhySec) implementations.
The paper focuses on the transfer of the IEEE802.11 (WLAN)
PhySec - Secret Key Generation (SKG) algorithms to Next Gen-
eration Mobile Networks (NGMNs), as they are the driving forces
and key enabler of future industrial networks. Based on a real
world Long Term Evolution (LTE) testbed, improvements of the
SKG algorithms are validated. The paper presents and evaluates
significant improvements in the establishment of channel profiles,
whereby especially the Bit Disagreement Rate (BDR) can be
improved substantially. The combination of the Discrete Cosine
Transformation (DCT) and the supervised Machine Learning
(ML) algorithm - Linear Regression (LR) – provides outstanding
results, which can be used beyond the SKG application. The
evaluation also emphasizes the appropriateness of PhySec for
securing private networks.