Machine Learning for Medium Access Control Protocol Recognition in Communications Networks
The ability to recognize the medium access control protocol employed by a network can facilitate the incorporation of a cognitive radio into an existing network by elucidating an integral aspect of network behavior. Since the way in which users access the electromagnetic spectrum is one of the most...
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2021-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9507498/ |
| _version_ | 1818425964942589952 |
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| author | Margaret M. Rooney Mark K. Hinders |
| author_facet | Margaret M. Rooney Mark K. Hinders |
| author_sort | Margaret M. Rooney |
| collection | DOAJ |
| description | The ability to recognize the medium access control protocol employed by a network can facilitate the incorporation of a cognitive radio into an existing network by elucidating an integral aspect of network behavior. Since the way in which users access the electromagnetic spectrum is one of the most prominent distinctions between reservation based and contention based medium access control protocols, the first part of this work exploits the regular timing of transmissions from networks utilizing reservation based time-division multiple access (TDMA) protocols to differentiate between transmissions governed by TDMA and by contention based carrier sense multiple access (CSMA) protocols. Our approach leverages modular arithmetic to identify periodicity in transmission timings and an unsupervised <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-means algorithm to generate distinct TDMA and CSMA clusters. Several supervised machine learning algorithms are explored to build a protocol classifier. We then present a method of distinguishing between transmissions from multi-channel frequency division multiple access (FDMA) based networks and single channel networks. This method uses an automated machine learning clustering algorithm to obtain an estimate of the actual center frequencies of channels utilized by a network. Such information can be used to determine whether the network is employing an FDMA protocol to access the electromagnetic spectrum. |
| first_indexed | 2024-12-14T14:22:19Z |
| format | Article |
| id | doaj.art-bc30a88db2b24111899d31d385b97e00 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T14:22:19Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-bc30a88db2b24111899d31d385b97e002022-12-21T22:58:03ZengIEEEIEEE Access2169-35362021-01-01911076211077110.1109/ACCESS.2021.31028599507498Machine Learning for Medium Access Control Protocol Recognition in Communications NetworksMargaret M. Rooney0https://orcid.org/0000-0001-7895-6262Mark K. Hinders1https://orcid.org/0000-0003-0846-9535Department of Applied Science, William & Mary, Williamsburg, VA, USADepartment of Applied Science, William & Mary, Williamsburg, VA, USAThe ability to recognize the medium access control protocol employed by a network can facilitate the incorporation of a cognitive radio into an existing network by elucidating an integral aspect of network behavior. Since the way in which users access the electromagnetic spectrum is one of the most prominent distinctions between reservation based and contention based medium access control protocols, the first part of this work exploits the regular timing of transmissions from networks utilizing reservation based time-division multiple access (TDMA) protocols to differentiate between transmissions governed by TDMA and by contention based carrier sense multiple access (CSMA) protocols. Our approach leverages modular arithmetic to identify periodicity in transmission timings and an unsupervised <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-means algorithm to generate distinct TDMA and CSMA clusters. Several supervised machine learning algorithms are explored to build a protocol classifier. We then present a method of distinguishing between transmissions from multi-channel frequency division multiple access (FDMA) based networks and single channel networks. This method uses an automated machine learning clustering algorithm to obtain an estimate of the actual center frequencies of channels utilized by a network. Such information can be used to determine whether the network is employing an FDMA protocol to access the electromagnetic spectrum.https://ieeexplore.ieee.org/document/9507498/Classificationclusteringmachine learningmedium access control protocolwireless communications |
| spellingShingle | Margaret M. Rooney Mark K. Hinders Machine Learning for Medium Access Control Protocol Recognition in Communications Networks IEEE Access Classification clustering machine learning medium access control protocol wireless communications |
| title | Machine Learning for Medium Access Control Protocol Recognition in Communications Networks |
| title_full | Machine Learning for Medium Access Control Protocol Recognition in Communications Networks |
| title_fullStr | Machine Learning for Medium Access Control Protocol Recognition in Communications Networks |
| title_full_unstemmed | Machine Learning for Medium Access Control Protocol Recognition in Communications Networks |
| title_short | Machine Learning for Medium Access Control Protocol Recognition in Communications Networks |
| title_sort | machine learning for medium access control protocol recognition in communications networks |
| topic | Classification clustering machine learning medium access control protocol wireless communications |
| url | https://ieeexplore.ieee.org/document/9507498/ |
| work_keys_str_mv | AT margaretmrooney machinelearningformediumaccesscontrolprotocolrecognitionincommunicationsnetworks AT markkhinders machinelearningformediumaccesscontrolprotocolrecognitionincommunicationsnetworks |