## Appendix A  
```
0:input_buffer output:results
1:Initialize an empty dictionary D:
2:\(D\leftarrow\varnothing\)
3:Split _input_buffer_ into easier to process tokens, then organize them into \(D\) with the frequency \(f_{i}\) as the key and the value as list of measured amplitudes \(A_{i}\) on that frequency in dBm:
4:\(i\in 1,2,\ldots,n\)
5:for each\(f_{i}\) in input_buffer:
6:for each\(A_{i,j}\) corresponding to \(f_{i}\):
7:\(A_{i}(j)\gets A_{i,j}\)
8:endfor
9:\(D(f_{i})\gets A_{i}\)
10:endfor
11:Initializing an empty dataframe \(Df\) with columns \"frequency\" and \"amplitude\", and members as data type float: \(Df\leftarrow\varnothing\)\(Df(0,0)\leftarrow\)\"frequency\"\(Df(0,1)\leftarrow\)\"amplitude\"
12:for each\(f_{i}\) in D: \(A_{mean}\gets average(A_{i})\)\(Df(i,0)\gets f_{i}\)\(Df(i,1)\gets A_{mean}\)
14:endfor
15: Sort D in order descending amplitude, and eliminate the rows where the frequency \(Df(i,0)<f_{start}\cdot 10^{6}\) or \(Df(i,0)>f_{stop}\cdot 10^{6}\) and the rows where the amplitude \(Df(i,1)<-68\) dBm
16: Separate the top 5 frequencies with the highest amplitudes in the list results:
17:\(results\leftarrow\varnothing\)
18:for i = 0,..., 4:
19:\(results(i)\gets Df(0,i+1)\)
20:endfor endfunction
```  
**Algorithm A1**Function _process_data_  
```
0:input_buffer output:results
1:Initialize an empty dictionary D:
2:\(D\leftarrow\varnothing\)
3:Split _input_buffer_ into easier to process tokens, then organize them into



## References

* [1][PERSON] Security threats against LTE networks: A survey. In _Security in Computing and Communications, Proceedings of the 6 th International Symposium, SSCC 2018, Bangalore, India, 19-22 September 2018_; Revised Selected Papers 6; Springer: Singapore, 2019; pp. 242-256. * [2] [PERSON]; [PERSON]; [PERSON]; [PERSON] Improving 4G/5G air interface security: A survey of existing attacks on different LTE layers. _Comput. Netw._**2021**, _201_, 108532. [CrossRef]
* [3] [PERSON]; [PERSON] Jamming attacks and anti-jamming strategies in wireless networks: A comprehensive survey. _IEEE Commun. Surv. Tutor._**2022**, _24_, 767-809. [CrossRef]
* [4] [PERSON]; [PERSON]; [PERSON] Microwave photonics approach as a novel smart fabrication technique of a radio communication jammers. _Procedia Comput. Sci._**2021**, _180_, 950-957. [CrossRef]
* [5] [PERSON] [PERSON]; [PERSON]; [PERSON]; [PERSON] Band & Tone Jamming Analysis and Detection on LoRa signals. _arXiv_**2021**, arXiv:2107.07782. * [6] [PERSON]; [PERSON]; [PERSON] Security vulnerabilities in LPWANs--An attack vector analysis for the IoT ecosystem. _Appl. Sci._**2021**, _11_, 3176. [CrossRef]
* [7] [PERSON]; [PERSON] [PERSON]; [PERSON] In-Depth Analysis of Signal Jammers' and Anti-Jamming Effect on 5G Signal. In Proceedings of the 2019 7 th International Conference on Future Internet of Things and Cloud Workshops (FiCloudW), Istanbul, Turkey, 26-28 August 2019; pp. 1-6. * [8] [PERSON]; [PERSON] Cascaded GSM Detector-Jammer Design. In Proceedings of the 2020 3 rd International Conference on Intelligent Sustainable Systems (ICISS), Thoothukudi, India, 3-5 December 2020; pp. 1471-1475. * [9] [PERSON]; [PERSON] Analytical Technique Leveraging Processing Gain for Evaluating the Anti-Jamming Potential of Underwater Acoustic Direct Sequence Spread Spectrum Communication Systems. _Symmetry_**2023**, _15_, 1710. [CrossRef]
* [10] [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] On the performance of deep reinforcement learning-based anti-jamming method confronting intelligent jammer. _Appl. Sci._**2019**, \(9\), 1361. [CrossRef]
* [11] [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Design and implementation of reinforcement learning-based intelligent jamming system. _IET Commun._**2020**, _14_, 3231-3238. [CrossRef]
* [12] [PERSON]; [PERSON]; [PERSON]; [PERSON] Intelligent Blocking System for Mobile Communications Initiated by Unauthorized Users.