Passive and Active Wireless Device Secure Identification
Secure wireless device identification is necessary if we want to ensure that any transmitted data reach only a desired receiver. However the fact that wireless communications are by nature broadcast creates unique challenges such as identity theft, eavesdropping for data interception, jamming attack...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9082628/ |
| _version_ | 1818664498116952064 |
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| author | Oscar Delgado Louai Kechtban Sebastien Lugan Benoit Macq |
| author_facet | Oscar Delgado Louai Kechtban Sebastien Lugan Benoit Macq |
| author_sort | Oscar Delgado |
| collection | DOAJ |
| description | Secure wireless device identification is necessary if we want to ensure that any transmitted data reach only a desired receiver. However the fact that wireless communications are by nature broadcast creates unique challenges such as identity theft, eavesdropping for data interception, jamming attacks to disrupt legitimate transmissions, etc. This paper proposes a new integrated radioprint framework (IRID) that has two main components. First, we propose a machine learning-based radio identification solution that relies on hardware variabilities of internal components of the transmitter caused during manufacturing, allowing us to achieve passive device identification. Second, we introduce a new kind of covert channel, based on variations in the emitted signal strength, which allows us to implement unique active device identification. We evaluate our proposed framework on an experimental test-bed of 20 identical WiFi devices. Although our experiments deal only with IEEE 802.11b, the approach can easily be extended to any wireless protocol. The experimental results show that our proposed solution can differentiate between network devices with accuracy in excess of 99% on the basis of a standard-compliant implementation. |
| first_indexed | 2024-12-17T05:33:41Z |
| format | Article |
| id | doaj.art-5e3f416dc6074bd5ae3b1cfb69aead05 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-17T05:33:41Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-5e3f416dc6074bd5ae3b1cfb69aead052022-12-21T22:01:40ZengIEEEIEEE Access2169-35362020-01-018833128332010.1109/ACCESS.2020.29916499082628Passive and Active Wireless Device Secure IdentificationOscar Delgado0https://orcid.org/0000-0001-9574-0850Louai Kechtban1https://orcid.org/0000-0003-1264-0112Sebastien Lugan2https://orcid.org/0000-0002-3553-987XBenoit Macq3https://orcid.org/0000-0002-7243-4778Department of Electrical and Computer Engineering, McGill University, Montreal, QC, CanadaInstitute of Information and Communication Technologies, Electronics, and Applied Mathematics (ICTEAM), Université Catholique de Louvain, Louvain-la-Neuve, BelgiumInstitute of Information and Communication Technologies, Electronics, and Applied Mathematics (ICTEAM), Université Catholique de Louvain, Louvain-la-Neuve, BelgiumInstitute of Information and Communication Technologies, Electronics, and Applied Mathematics (ICTEAM), Université Catholique de Louvain, Louvain-la-Neuve, BelgiumSecure wireless device identification is necessary if we want to ensure that any transmitted data reach only a desired receiver. However the fact that wireless communications are by nature broadcast creates unique challenges such as identity theft, eavesdropping for data interception, jamming attacks to disrupt legitimate transmissions, etc. This paper proposes a new integrated radioprint framework (IRID) that has two main components. First, we propose a machine learning-based radio identification solution that relies on hardware variabilities of internal components of the transmitter caused during manufacturing, allowing us to achieve passive device identification. Second, we introduce a new kind of covert channel, based on variations in the emitted signal strength, which allows us to implement unique active device identification. We evaluate our proposed framework on an experimental test-bed of 20 identical WiFi devices. Although our experiments deal only with IEEE 802.11b, the approach can easily be extended to any wireless protocol. The experimental results show that our proposed solution can differentiate between network devices with accuracy in excess of 99% on the basis of a standard-compliant implementation.https://ieeexplore.ieee.org/document/9082628/Radioprintmachine learningcovert channelPUFWiFi80211 |
| spellingShingle | Oscar Delgado Louai Kechtban Sebastien Lugan Benoit Macq Passive and Active Wireless Device Secure Identification IEEE Access Radioprint machine learning covert channel PUF WiFi 80211 |
| title | Passive and Active Wireless Device Secure Identification |
| title_full | Passive and Active Wireless Device Secure Identification |
| title_fullStr | Passive and Active Wireless Device Secure Identification |
| title_full_unstemmed | Passive and Active Wireless Device Secure Identification |
| title_short | Passive and Active Wireless Device Secure Identification |
| title_sort | passive and active wireless device secure identification |
| topic | Radioprint machine learning covert channel PUF WiFi 80211 |
| url | https://ieeexplore.ieee.org/document/9082628/ |
| work_keys_str_mv | AT oscardelgado passiveandactivewirelessdevicesecureidentification AT louaikechtban passiveandactivewirelessdevicesecureidentification AT sebastienlugan passiveandactivewirelessdevicesecureidentification AT benoitmacq passiveandactivewirelessdevicesecureidentification |