Learning-Aided Physical Layer Authentication as an Intelligent Process

Abstract

Performance of the existing physical layer authentication schemes could be severely affected by the imperfect estimates and variations of the communication link attributes used. The commonly adopted static hypothesis testing for physical layer authentication faces significant challenges in time-varying communication channels due to the changing propagation and interference conditions, which are typically unknown at the design stage. To circumvent this impediment, we propose an adaptive physical layer authentication scheme based on machine-learning as an intelligent process to learn and utilize the complex and time-varying environment, and hence to improve the reliability and robustness of physical layer authentication. Explicitly, a physical layer attribute fusion model based on a kernel machine is designed for dealing with multiple attributes without requiring the knowledge of their statistical properties. By modeling the physical layer authentication as a linear system, the proposed technique directly reduces the authentication scope from a combined N -dimensional feature space to a singledimensional (scalar) space, hence leading to reduced authentication complexity. By formulating the learning (training) objective of the physical layer authentication as a convex problem, an adaptive algorithm based on kernel least-mean-square is then proposed as an intelligent process to learn and track the variations of multiple attributes, and therefore to enhance the authentication performance.Both the convergence and the authentication performance of the proposed intelligent authentication process are theoretically analyzed. Our simulations demonstrate that our solution significantly improves the authentication performance in time-varying environments.

Statistical and Machine Learning-Based Decision Techniques for Physical Layer Authentication

“…In [17], k-NN algorithms are used to identify electronic devices through their radio-frequency emissions, and the presence of an attacker is not explicitly considered. Authors of [18] propose the application of an adaptive algorithm for PLA in a dynamically changing wireless environment, considering a single channel model and a series of physical layer attributes which may include CSI. In [19], instead, machine-learningaided intelligent authentication techniques are proposed for 5G communications.…”

“…This models a more realistic training phase with respect to [18]. Moreover, differently from [18], we also consider the use of non-supervised techniques to discriminate authentic from forged messages during the training phase, letting a clustering algorithm to decide about the nature of the received packets. We show that even in such an unfavorable condition it is possible to achieve good security performance using machine learning algorithms, especially when the attacker's channel has a low spatial correlation with the main one.…”

Machine Learning for Intelligent Authentication in 5G and Beyond Wireless Networks

“…The 5G-and-beyond wireless networks have received tremendous attentions in both academia and industries alike, which will enable a wide variety of vertical applications by connecting significant communication overhead. Furthermore, due to the disregard of inherent features of communicating devices, detecting compromised security keys cannot be readily achieved by the conventional digital credentials-based techniques [2].…”

“…1: Alice-Bob-Eve model in the complex time-varying environment. Bob identifies Alice from Spoofer based on the multi-dimensional received information, which may be related to communication links, physical environment, and communication process, just to name a few.Although cryptographic techniques have been widely studied for authentication, they may fall short of the desired performance in many emerging scenarios of 5G-and-beyond wireless networks [2]. The fundamental weaknesses of conventional cryptography techniques are the increasing latencies, communication and computation overheads for achieving better security performance, which are extremely undesirable for the delay-sensitive communications and resource-constraint devices.…”

“…Although cryptographic techniques have been widely studied for authentication, they may fall short of the desired performance in many emerging scenarios of 5G-and-beyond wireless networks [2]. The fundamental weaknesses of conventional cryptography techniques are the increasing latencies, communication and computation overheads for achieving better security performance, which are extremely undesirable for the delay-sensitive communications and resource-constraint devices.…”