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

Abstract

The fifth generation (5G) and beyond wireless networks are critical to support diverse vertical applications by connecting heterogeneous devices and machines, which directly increase vulnerability for various spoofing attacks. Conventional cryptographic and physical layer authentication techniques are facing some challenges in complex dynamic wireless environments, including significant security overhead, low reliability, as well as difficulties in pre-designing a precise authentication model, providing continuous protection, and learning time-varying attributes. In this article, we envision new authentication approaches based on machine learning techniques by opportunistically leveraging physical layer attributes, and introduce intelligence to authentication for more efficient security provisioning. Machine learning paradigms for intelligent authentication design are presented, namely for parametric/non-parametric and supervised/unsupervised/reinforcement learning algorithms. In a nutshell, the machine learning-based intelligent authentication approaches utilize specific features in the multi-dimensional domain for achieving cost-effective, more reliable, model-free, continuous, and situation-aware device validation under unknown network conditions and unpredictable dynamics. 2 heterogeneous devices and machines [1]. The dramatically growing number of low-cost devices and access points with increased mobility and heterogeneity leads to the extremely complex and dynamic environment of 5G-and-beyond wireless networks. Specifically, due to the open broadcast nature of radio signal propagation, widely adopted standardized transmission protocols, and intermittent communication characteristic, wireless communications are extremely vulnerable to interception and spoofing attacks [2]. As shown in the conventional 'Alice-Bob-Eve' model of Fig. 1, Alice and Bob are legitimate devices and communicate with each other in the presence of a Spoofer (Eve). This adversary dynamically intends to intercept legitimate communications and to impersonate Alice for obtaining illegal advantages from Bob. The main objective of Bob is to uniquely and unambiguously identify Alice by authentication techniques. Multi-dimensional information received at Bob Time Spoofing Signal Legitimate Signal Scattering cluster Scattering cluster Scattering cluster Eve (Spoofer) Alice Bob Process-related information Physical information Fig. 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 pe...

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“…Parametric methods have limitations such as "considering a default distribution PLOS ONE for the dependent variable", "linearity of the proposed relationship", "uniform variance of errors", and "independency of the variables" [107]. However, nonparametric methods do not have these limitations [108]. It should be noted that the analysis of complex ecological data requires robust and flexible analytical methods that control nonlinear relationships, interactions, and missing data.…”

Adaptive Trust Management for Soft Authentication and Progressive Authorization Relying on Physical Layer Attributes

“…According to the derived R ind [1] and S ind [1] in (25) and (26), respectively, the the individual risk and individual satisfaction of our scheme at time instant t = 2, 3, 4, ... are given as (27) and (28), respectively, under condition ν 1 ≥ /2.…”

“…I NNOVATIONS in wireless communications and Internet technologies during last few decades have brought about not only radically new applications, but also significantly increased security challenges imposed on the legitimate users owing to the rapidly improving capability of adversaries. To be specific, due to the broadcast nature of radio signal propagation, owing to the intermittent nature of communications as well as the complex dynamic network environments encountered, wireless communications are vulnerable to spoofing H. Fang attacks [1]- [3]. A spoofer may intercept the transmissions between legitimate devices and imitate them to obtain illegal benefits from networks/systems, while counterfeiting authorized identities for fraud or other malicious purposes.…”

“…These security schemes cannot help in detecting/preventing spoofers after the initial authentication has been completed. Although repeated authentication may theoretically be achieved with the aid of key-based cryptographic techniques by repeatedly logging into the server/system, the excessive latencies and computational overhead are particularly undesirable for delay-sensitive communications as well as for devices having limited battery lifetime and computational capability, such as the Internetof-Thing (IoT) devices [1]- [3].…”

“…More importantly, our scheme moves further away from the classical mechanisms, since it quests a holistic system design of unified authentication and authorization based on the continuous evaluation of time-varying physical layer attribute, which requires new wireless radio technologies. Hence, the machine learning techniques [1], [2] are studied in this paper for adaptive trust management through classifying the time-varying attribute estimates of the transmitter.…”