Concept Drift Aware Wireless Key Generation in Dynamic LiFi Networks
This paper studies the generation of cryptographic keys from wireless channels in light-fidelity (LiFi) networks. Unlike existing studies, we account for several practical considerations (a) realistic indoor multi-user mobility scenarios, (b) non-ideal channel reciprocity given the unique characteri...
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2025-01-01
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| Series: | IEEE Open Journal of the Communications Society |
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| Online Access: | https://ieeexplore.ieee.org/document/10818749/ |
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| author | Elmahedi Mahalal Eslam Hasan Muhammad Ismail Zi-Yang Wu Mostafa M. Fouda Zubair Md Fadlullah Nei Kato |
| author_facet | Elmahedi Mahalal Eslam Hasan Muhammad Ismail Zi-Yang Wu Mostafa M. Fouda Zubair Md Fadlullah Nei Kato |
| author_sort | Elmahedi Mahalal |
| collection | DOAJ |
| description | This paper studies the generation of cryptographic keys from wireless channels in light-fidelity (LiFi) networks. Unlike existing studies, we account for several practical considerations (a) realistic indoor multi-user mobility scenarios, (b) non-ideal channel reciprocity given the unique characteristics of the downlink visible light (VL) and uplink infrared (IR) channels, (c) different room occupancy levels, (d) different room layouts, and (e) different receivers’ field-of-view (FoV). Since general channel models in dynamic LiFi networks are inaccurate, we propose a novel deep learning-based framework to generate secret keys with minimal key disagreement rate (KDR) and maximal key generation rate (KGR). However, we find that wireless channels in LiFi networks exhibit different statistical behaviors under various conditions, leading to concept drift in the deep learning model. As a result, key generation suffers from (a) a deterioration in KDR and KGR up to 29% and 38%, respectively, and (b) failing the NIST randomness test. To enable a concept drift aware framework, we propose an adaptive learning strategy using the similarity of channel probability density functions and the mix-of-experts ensemble method. Results show our adaptive learning strategy can achieve stable performance that passes the NIST randomness test and achieves 8% KDR and 89 bits/s KGR for a case of study with 60° FoV. |
| format | Article |
| id | doaj-art-9c04eb37d0ad49bebb206764cb9b5bd9 |
| institution | DOAJ |
| issn | 2644-125X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of the Communications Society |
| spelling | doaj-art-9c04eb37d0ad49bebb206764cb9b5bd92025-08-20T03:18:07ZengIEEEIEEE Open Journal of the Communications Society2644-125X2025-01-01674275810.1109/OJCOMS.2024.352449710818749Concept Drift Aware Wireless Key Generation in Dynamic LiFi NetworksElmahedi Mahalal0https://orcid.org/0000-0001-7248-1470Eslam Hasan1https://orcid.org/0000-0001-7385-2632Muhammad Ismail2https://orcid.org/0000-0002-8051-9747Zi-Yang Wu3https://orcid.org/0000-0002-5334-3686Mostafa M. Fouda4https://orcid.org/0000-0003-1790-8640Zubair Md Fadlullah5https://orcid.org/0000-0002-4785-2425Nei Kato6https://orcid.org/0000-0001-8769-302XDepartment of Electrical, Computer Engineering and Computer Science, University of New Haven, West Haven, CT, USADepartment of Computer Science, Tennessee Tech University, Cookeville, TN, USADepartment of Computer Science, Tennessee Tech University, Cookeville, TN, USACollege of Information Science and Engineering, Northeastern University, Shenyang, ChinaDepartment of Electrical and Computer Engineering, Idaho State University, Pocatello, ID, USADepartment of Computer Science, Western University, London, ON, CanadaGraduate School of Information Sciences, Tohoku University, Sendai, JapanThis paper studies the generation of cryptographic keys from wireless channels in light-fidelity (LiFi) networks. Unlike existing studies, we account for several practical considerations (a) realistic indoor multi-user mobility scenarios, (b) non-ideal channel reciprocity given the unique characteristics of the downlink visible light (VL) and uplink infrared (IR) channels, (c) different room occupancy levels, (d) different room layouts, and (e) different receivers’ field-of-view (FoV). Since general channel models in dynamic LiFi networks are inaccurate, we propose a novel deep learning-based framework to generate secret keys with minimal key disagreement rate (KDR) and maximal key generation rate (KGR). However, we find that wireless channels in LiFi networks exhibit different statistical behaviors under various conditions, leading to concept drift in the deep learning model. As a result, key generation suffers from (a) a deterioration in KDR and KGR up to 29% and 38%, respectively, and (b) failing the NIST randomness test. To enable a concept drift aware framework, we propose an adaptive learning strategy using the similarity of channel probability density functions and the mix-of-experts ensemble method. Results show our adaptive learning strategy can achieve stable performance that passes the NIST randomness test and achieves 8% KDR and 89 bits/s KGR for a case of study with 60° FoV.https://ieeexplore.ieee.org/document/10818749/Concept driftchannel reciprocitydeep learninginfrared channelkey disagreement rate (KDR)key generation rate (KGR) |
| spellingShingle | Elmahedi Mahalal Eslam Hasan Muhammad Ismail Zi-Yang Wu Mostafa M. Fouda Zubair Md Fadlullah Nei Kato Concept Drift Aware Wireless Key Generation in Dynamic LiFi Networks IEEE Open Journal of the Communications Society Concept drift channel reciprocity deep learning infrared channel key disagreement rate (KDR) key generation rate (KGR) |
| title | Concept Drift Aware Wireless Key Generation in Dynamic LiFi Networks |
| title_full | Concept Drift Aware Wireless Key Generation in Dynamic LiFi Networks |
| title_fullStr | Concept Drift Aware Wireless Key Generation in Dynamic LiFi Networks |
| title_full_unstemmed | Concept Drift Aware Wireless Key Generation in Dynamic LiFi Networks |
| title_short | Concept Drift Aware Wireless Key Generation in Dynamic LiFi Networks |
| title_sort | concept drift aware wireless key generation in dynamic lifi networks |
| topic | Concept drift channel reciprocity deep learning infrared channel key disagreement rate (KDR) key generation rate (KGR) |
| url | https://ieeexplore.ieee.org/document/10818749/ |
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