# Physical Layer Authentication in Wireless Networks-Based Machine Learning Approaches
## 4 Research Methodology
### Phase 2. Conducting the Review
**Stage 2:** In this stage, to restrict the number of studies for review, we adopted inclusion and exclusion criteria, as mentioned in Table 3.  
**Stage 3:** We eliminated duplicate studies from the set of 248 papers. Figure 3: Primary study selection process. **Step 4. Study Quality Assessment:**  
An essential part of writing an SLR paper is choosing high-quality literature to make the most accurate and reliable review. Quality assessment rules (QARs) were applied using a group of research questions to assess the quality of studies. Ten QARs were classified, each worth one point out of 10. The rate of each QAR was selected as follows: completely answered = 1, above-average answer rate = 0.75, average answer rate = 0.5, below-average answer rate = 0.25, completely unanswered = 0. If the summation of all ten QARs was less than 5, the study was excluded. Figure 4: Distribution of papers by publisher. Figure 5: Distribution of publications by year. \begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Inclusion Criteria** & **Exclusion Criteria** \\ \hline Studies related to physical layer authentication and wireless network or IoT security. & Studies written in languages other than English. \\ Studies related to transmitter classification and using deep learning techniques or machine learning techniques. & Studies that are reviews or surveys without findings. \\ Studies published from January 2015 to January 2022. & Studies without justifiable research contributions. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Inclusion and exclusion criteria to select the primary studies. The quality assessment rules used to assess the quality of the studies in Table 4 were as follows:  
QA1: Are the research objectives clearly explained? QA2: Is the study set in the context of other studies and research? QA3: Is the specific field of wireless network security clearly defined? QA4: Is the proposed system model clearly identified and acceptable? QA5: Are the ML/DL techniques appropriately described? QA6: Is the design of the experiment appropriate and adequate? QA7: Was the study executed on a sufficient dataset? QA8: Is the implemented experiment appropriately described? QA9: Are the results of the experiments well justified? QA10: Do the results support the findings presented? The scoring results from applying the QARs are shown in Table 5. \begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Study Number** & **Title** & **Authors** & **Year** & **Publisher** & **Type** \\ \hline S1 & Radio Frequency Fingerprint Based Wireless Transmitter Identification Against Malicious Attacker: An Adversarial Learning Approach & [PERSON] et al. [70] & 2020 & IEEE & Conference \\ S2 & RFAL: Adversarial Learning for RF Transmitter Identification and Classification & [PERSON] et al. [71] & 2020 & IEEE & Journal \\ S3 & Penetrating RF Fingerprinting-based Authentication with a Generative Adversarial Attack & [PERSON] et al. [72] & 2021 & IEEE & Conference \\ S4 & New Security Mechanisms of High-Reliability IoT Communication Based on Radio Frequency Fingerprint & [PERSON] et al. [73] & 2019 & IEEE & Journal \\ S5 & Improving Wireless Network Security Based On Radio Fingerprinting & [PERSON] et al. [74] & 2019 & IEEE & Conference \\ S6 & Identification and authentication for wireless transmission security based on RF-DNA fingerprint & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Studies that met the quality evaluation criteria. Figure 6: Distribution by type. * _Cont._