DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lakshmanan, Nitya | ko |
dc.contributor.author | Budhdev, Nishant | ko |
dc.contributor.author | Kang, Min Suk | ko |
dc.contributor.author | Chan, Mun Choon | ko |
dc.contributor.author | Han, Jun | ko |
dc.date.accessioned | 2021-08-11T00:30:16Z | - |
dc.date.available | 2021-08-11T00:30:16Z | - |
dc.date.created | 2020-11-09 | - |
dc.date.created | 2020-11-09 | - |
dc.date.created | 2020-11-09 | - |
dc.date.issued | 2021-08-11 | - |
dc.identifier.citation | USENIX Security Symposium, pp.3899 - 3916 | - |
dc.identifier.uri | http://hdl.handle.net/10203/287130 | - |
dc.description.abstract | We present the SLIC that achieves fine-grained location tracking(e.g., finding indoor walking paths) of targeted cellular user devices in a passive manner. The attack exploits a new side channel in modern cellular systems through a universally available feature called carrier aggregation (CA). CA enables higher cellular data rates by allowing multiple base stations on different carrier frequencies to concurrently transmit to a single user. We discover that a passive adversary can learn the side channel—namely, the number of actively transmitting base stations for any user of interest in the same macrocell. We then show that a time series of this side channel can constitute a highly unique fingerprint of a walking path, which can be used to identify the path taken by a target cellular user. We first demonstrate the collection of the new side channel and a small-scale path identification attack in an existing LTE-A network with up to three CA capability (i.e., three base stations can be coordinated for concurrent transmission), showing the feasibility of SLIC in the current cellular networks. We then emulate a near-future 5G network environment with up to nine CA capability in various multi-story buildings in our institution. SLIC shows up to 98.4% of path-identification accuracy among 100 different walking paths in a large office building. Through testing in various building structures, we confirm that the attack is effective in typical office building environments; e.g., corridors, open spaces. We present complete and partial countermeasures and discuss some practical cell deployment suggestions for 5G networks. | - |
dc.language | English | - |
dc.publisher | USENIX | - |
dc.title | A Stealthy Location Identification Attack Exploiting Carrier Aggregation in Cellular Networks | - |
dc.type | Conference | - |
dc.identifier.wosid | 000722006804013 | - |
dc.identifier.scopusid | 2-s2.0-85114451315 | - |
dc.type.rims | CONF | - |
dc.citation.beginningpage | 3899 | - |
dc.citation.endingpage | 3916 | - |
dc.citation.publicationname | USENIX Security Symposium | - |
dc.identifier.conferencecountry | US | - |
dc.identifier.conferencelocation | Virtual | - |
dc.contributor.localauthor | Kang, Min Suk | - |
dc.contributor.nonIdAuthor | Lakshmanan, Nitya | - |
dc.contributor.nonIdAuthor | Budhdev, Nishant | - |
dc.contributor.nonIdAuthor | Chan, Mun Choon | - |
dc.contributor.nonIdAuthor | Han, Jun | - |
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