#PrettyFlyForAWiFi: real-world detection of privacy invasion attacks by drones
Drones are becoming increasingly popular for hobbyists and recreational use. But with this surge in popularity comes increased risk to privacy as the technology makes it easy to spy on people in otherwise-private environments, such as an individual’s home. An attacker can fly a drone over fences and...
| Main Authors: | , , , |
|---|---|
| Format: | Journal article |
| Language: | English |
| Published: |
Association for Computing Machinery
2021
|
| _version_ | 1797053747816300544 |
|---|---|
| author | Birnbach, S Baker, R Eberz, S Martinovic, I |
| author_facet | Birnbach, S Baker, R Eberz, S Martinovic, I |
| author_sort | Birnbach, S |
| collection | OXFORD |
| description | Drones are becoming increasingly popular for hobbyists and recreational use. But with this surge in popularity comes increased risk to privacy as the technology makes it easy to spy on people in otherwise-private environments, such as an individual’s home. An attacker can fly a drone over fences and walls to observe the inside of a house, without having physical access. Existing drone detection systems require specialist hardware and expensive deployment efforts, making them inaccessible to the general public.
In this work, we present a drone detection system that requires minimal prior configuration and uses inexpensive commercial off-the-shelf hardware to detect drones that are carrying out privacy invasion attacks. We use a model of the attack structure to derive statistical metrics for movement and proximity that are then applied to received communications between a drone and its controller. We test our system in real-world experiments with two popular consumer drone models mounting privacy invasion attacks using a range of flight patterns. We are able both to detect the presence of a drone and to identify which phase of the privacy attack was in progress while being resistant to false positives from other mobile transmitters. For line-of-sight approaches using our kurtosis-based method, we are able to detect all drones at a distance of 6 m, with the majority of approaches detected at 25 m or farther from the target window without suffering false positives for stationary or mobile non-drone transmitters. |
| first_indexed | 2024-03-06T18:48:02Z |
| format | Journal article |
| id | oxford-uuid:0f37ce7d-6d6e-43b4-8d36-e79082f0b1dc |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-06T18:48:02Z |
| publishDate | 2021 |
| publisher | Association for Computing Machinery |
| record_format | dspace |
| spelling | oxford-uuid:0f37ce7d-6d6e-43b4-8d36-e79082f0b1dc2022-03-26T09:50:05Z#PrettyFlyForAWiFi: real-world detection of privacy invasion attacks by dronesJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:0f37ce7d-6d6e-43b4-8d36-e79082f0b1dcEnglishSymplectic ElementsAssociation for Computing Machinery2021Birnbach, SBaker, REberz, SMartinovic, IDrones are becoming increasingly popular for hobbyists and recreational use. But with this surge in popularity comes increased risk to privacy as the technology makes it easy to spy on people in otherwise-private environments, such as an individual’s home. An attacker can fly a drone over fences and walls to observe the inside of a house, without having physical access. Existing drone detection systems require specialist hardware and expensive deployment efforts, making them inaccessible to the general public. In this work, we present a drone detection system that requires minimal prior configuration and uses inexpensive commercial off-the-shelf hardware to detect drones that are carrying out privacy invasion attacks. We use a model of the attack structure to derive statistical metrics for movement and proximity that are then applied to received communications between a drone and its controller. We test our system in real-world experiments with two popular consumer drone models mounting privacy invasion attacks using a range of flight patterns. We are able both to detect the presence of a drone and to identify which phase of the privacy attack was in progress while being resistant to false positives from other mobile transmitters. For line-of-sight approaches using our kurtosis-based method, we are able to detect all drones at a distance of 6 m, with the majority of approaches detected at 25 m or farther from the target window without suffering false positives for stationary or mobile non-drone transmitters. |
| spellingShingle | Birnbach, S Baker, R Eberz, S Martinovic, I #PrettyFlyForAWiFi: real-world detection of privacy invasion attacks by drones |
| title | #PrettyFlyForAWiFi: real-world detection of privacy invasion attacks by drones |
| title_full | #PrettyFlyForAWiFi: real-world detection of privacy invasion attacks by drones |
| title_fullStr | #PrettyFlyForAWiFi: real-world detection of privacy invasion attacks by drones |
| title_full_unstemmed | #PrettyFlyForAWiFi: real-world detection of privacy invasion attacks by drones |
| title_short | #PrettyFlyForAWiFi: real-world detection of privacy invasion attacks by drones |
| title_sort | prettyflyforawifi real world detection of privacy invasion attacks by drones |
| work_keys_str_mv | AT birnbachs prettyflyforawifirealworlddetectionofprivacyinvasionattacksbydrones AT bakerr prettyflyforawifirealworlddetectionofprivacyinvasionattacksbydrones AT eberzs prettyflyforawifirealworlddetectionofprivacyinvasionattacksbydrones AT martinovici prettyflyforawifirealworlddetectionofprivacyinvasionattacksbydrones |