# Physical Layer Authentication in Wireless Networks-Based Machine Learning Approaches
## 5 Analysis
#### 5.2.1 Review of Selected PLA Technique-based Radio Frequency Fingerprinting
Further, in **(S1)**, a multi-classifier approach was presented to detect adversaries and classify trusted transmitters based on I/Q imbalance;, where I/Q imbalance is defined as the unique RF fingerprint of the different wireless transmitters. The classifier consists of multiple binary classifiers. Finally, the proposed method classifieds trusted transmitters with an average accuracy of 97.36% and detected adversaries with 99.98% accuracy. Traditional cyberattack methods apply to IoT systems; therefore, security threat problems are significant obstacles to IoT development. Most IoT networks form a star network topology, and many devices connect to a central smart hub gateway that can act as the authenticator. The study **(S8)** established the RF authentication model by using RFF to effectively distinguish between authentic and rogue devices in the mobile IoT. In addition, they used methods in the authentication model--neighbourhood component analysis, support vector data description, and the swarm optimization algorithm. The model results showed that when the SNR exceeded 15 dB, the authentication success rate of this method exceeded 90%. In **(S4)**, a new access authentication method for IoT devices was proposed based on RFF technology. The identification process of wireless devices includes signal acquisition, using Hilbert transform to extract the signal feature, principal component analysis to reduce dimensionality, RFF for forming, and, finally, the KNN classifier for wireless device identification. The MITM attack is a common security problem that can damage industrial IoT. When an MITM attack was simulated in the study, the authors indicated that when the SNR was 10 dB, the attack detection probability was close to 100% and the authentication success rate was greater than 95%. Cryptography based on a public key algorithm is not readily available for low-cost devices; for example, the processor of the NFC tag usually does not have enough computational power to process the public key algorithm. NFC security standards require public-key encryption/decryption to solve this issue, in terms of confidentiality and authenticity, and multiple attempts have been made to improve NFC's authenticator. However, adversaries can exploit weaknesses in the stream cipher to read and modify memory blocks of an NFC tag. In NFC, a tag responds immediately to an NFC reader's initiation, if the reader and the tag are nearby. In **(S15)**, the proposed identification scheme for NFC tags used RF fingerprinting, in which the goal was to enhance the security of NFC by preventing a cloning attack. Through evaluation, they confirmed that all considered deep learning structures operating in the proposed identification scheme achieved the highest accuracy for tag identification, with around 96%. Traditional RFF techniques are becoming less prominent due to advanced manufacturing techniques, which have resulted in certain limitations. To address the issues of low reliability, reduced user capacity, diminishing distinguishability, and complications in the data processing of RFFs, **(S9)** proposed a novel injectable RFF scheme through electromagnetic metasurfaces. The authors created a metasurface method for radio frequency fingerprint injection (MeRFFI) to make small but detectable perturbations in the specific frequency band in which IoT devices communicate. MeRFFI is designed for stationary IoT devices that communicate over wideband RF channels and is perfect for systems that require strong security, such as printers, health devices, wireless cameras, sensor network backhaul, and industrial monitoring systems. The method is incredibly energy efficient, as it can operate on zero power or a few hundred microjoules, depending on the application. The authors of **(S31)** developed a lightweight machine learning framework that compensates for receiver non-idealities and accounts for both data variability and channel variability simultaneously.