Traffic Analysis Through Spatial and Temporal Correlation: Threat and Countermeasure
| dc.contributor.author | Ebrahimi, Yousef | |
| dc.contributor.author | Younis, Mohamed | |
| dc.date.accessioned | 2021-05-13T15:27:31Z | |
| dc.date.available | 2021-05-13T15:27:31Z | |
| dc.date.issued | 2021-04-05 | |
| dc.description.abstract | The base station (BS) in a Wireless Sensor Network (WSN) plays the role of a data sink, a point of contact with the upper hierarchy, and an in-situ command and control unit. Such an essential role makes the BS a target for attacks in a hostile environment. Even if its presence is camouflaged, an adversary may locate the BS by applying traffic analysis. Basically, the adversary can intercept radio transmissions and correlate them using techniques like Evidence theory (ET). The ET attack model only uses spatial aspects of intercepted transmissions in order to deduce knowledge about data routes. In this paper, we propose an enhanced version of ET (EET) which utilizes temporal correlation of transmissions to draw further valuable insight about the network topology. Analyzing ET and extending its capability are very fundamental for the network in order to avoid the illusive sense of security by guarding against a weaker attack model than what could be potentially launched. Moreover, we develop a novel and effective countermeasure, called Assisted Deception (AD) that needs no involvement of BS and is resilient to both ET and EET. By implementing AD, nodes coordinate and inject timed deceptive packets to target temporal correlation of consecutive transmissions that EET relies on. The attack and countermeasure are validated through extensive simulation experiments. | en_US |
| dc.description.uri | https://ieeexplore.ieee.org/document/9395094 | en_US |
| dc.format.extent | 26 pages | en_US |
| dc.genre | journal articles | en_US |
| dc.identifier | doi:10.13016/m2bql5-c1ks | |
| dc.identifier.citation | Y. Ebrahimi and M. Younis, "Traffic Analysis Through Spatial and Temporal Correlation: Threat and Countermeasure," in IEEE Access, vol. 9, pp. 54126-54151, 2021, doi: 10.1109/ACCESS.2021.3070841. | en_US |
| dc.identifier.uri | https://doi.org/10.1109/ACCESS.2021.3070841 | |
| dc.identifier.uri | http://hdl.handle.net/11603/21515 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | IEEE | en_US |
| dc.relation.isAvailableAt | The University of Maryland, Baltimore County (UMBC) | |
| dc.relation.ispartof | UMBC Computer Science and Electrical Engineering Department Collection | |
| dc.relation.ispartof | UMBC Faculty Collection | |
| dc.relation.ispartof | UMBC Student Collection | |
| dc.relation.ispartof | UMBC College of Engineering and Information Technology Dean's Office | |
| dc.rights | This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author. | |
| dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 International | * |
| dc.rights.uri | https://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
| dc.title | Traffic Analysis Through Spatial and Temporal Correlation: Threat and Countermeasure | en_US |
| dc.type | Text | en_US |