Blind detection: Advanced techniques for WiFi-based drone surveillance

The great availability of commercial drones has raised growing interest among people, since remotely piloted vehicles can be employed in numerous applications. The pervasive use of these devices has created many privacy and safety concerns that need to be addressed by means of proper surveillance systems able to cope with such threats. In this paper, we propose a WiFi statistical fingerprint-based drone detection approach, which is capable of identifying nearby drone threats, even in the presence of malicious attacks. We present a performance analysis carried out through experimental tests, where our solution is able to achieve very good results in terms of correct recognitions in many real-life scenarios, with a peak true positive rate of 96%. © 1967-2012 IEEE.

Авторы
Bisio I. 1, 2 , Garibotto C.1 , Lavagetto F.1 , Sciarrone A.1 , Zappatore S.1
Издательство
Institute of Electrical and Electronics Engineers Inc.
Номер выпуска
1
Язык
Английский
Страницы
938-946
Статус
Опубликовано
Номер
8556480
Том
68
Год
2019
Организации
  • 1 DITEN Department, University of Genova, Genova, 16145, Italy
  • 2 Peoples' Friendship University of Russia (RUDN University), Moscow, 117198, Russian Federation
Ключевые слова
data mining; Drones; fingerprint; statistical analysis; UAVs; WiFi
Дата создания
04.02.2019
Дата изменения
04.02.2019
Постоянная ссылка
https://repository.rudn.ru/ru/records/article/record/36125/