Abstract
Eavesdropping is a fundamental threat to the security and privacy of wireless networks. This paper presents EarFisher - the first system that can detect wireless eavesdroppers and differentiate them from legitimate receivers. EarFisher achieves this by stimulating wireless eavesdroppers using bait network traffic, and then capturing eavesdroppers' responses by sensing and analyzing their memory EMRs. Extensive experiments show that EarFisher accurately detects wireless eavesdroppers even under poor signal conditions, and is resilient to the interference of system memory workloads, high volumes of normal network traffic, and the memory EMRs emitted by coexisting devices. We then further propose a method to detect eavesdropper's countermeasure, which deliberately emits strong memory EMR to interfere with EarFisher's detection.
| Original language | English |
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| Title of host publication | Proceedings of the 18th USENIX Symposium on Networked Systems Design and Implementation |
| Publisher | USENIX Association |
| Pages | 873-886 |
| ISBN (Electronic) | 9781939133212 |
| ISBN (Print) | 9781713829065 |
| Publication status | Published - Apr 2021 |
| Externally published | Yes |
| Event | 18th USENIX Symposium on Networked Systems Design and Implementation (NSDI 2021) - virtual Duration: 12 Apr 2021 → 14 Apr 2021 https://www.usenix.org/sites/default/files/nsdi21_proceedings_cover.pdf |
Publication series
| Name | Proceedings of the USENIX Symposium on Networked Systems Design and Implementation, NSDI |
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Conference
| Conference | 18th USENIX Symposium on Networked Systems Design and Implementation (NSDI 2021) |
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| Period | 12/04/21 → 14/04/21 |
| Internet address |
Funding
We are grateful to NSDI reviewers and our shepherd, Andreas Haeberlen, for their insightful comments. This research was supported, in part, by funds from BvTech S.p.A. and the members of the Cybersecurity at MIT Sloan (CAMS) consortium (https://cams.mit.edu)