دورية أكاديمية

Intrusion Detection Framework for Invasive FPV Drones Using Video Streaming Characteristics

التفاصيل البيبلوغرافية
العنوان: Intrusion Detection Framework for Invasive FPV Drones Using Video Streaming Characteristics
المؤلفون: Alsoliman A., Rigoni G., Callegaro D., Levorato M., Pinotti C. M., Conti M.
المساهمون: Alsoliman, A., Rigoni, G., Callegaro, D., Levorato, M., Pinotti, C. M., Conti, M.
بيانات النشر: ASSOC COMPUTING MACHINERY
سنة النشر: 2023
المجموعة: Padua Research Archive (IRIS - Università degli Studi di Padova)
مصطلحات موضوعية: Drone detection, drone video streaming, experimental testbed
الوصف: Cheap commercial off-the-shelf (COTS) First-Person View (FPV) drones have become widely available for consumers in recent years. Unfortunately, they also provide low-cost attack opportunities to malicious users. Thus, effective methods to detect the presence of unknown and non-cooperating drones within a restricted area are highly demanded. Approaches based on detection of drones based on emitted video stream have been proposed, but were not yet shown to work against other similar benign traffic, such as that generated by wireless security cameras. Most importantly, these approaches were not studied in the context of detecting new unprofiled drone types. In this work, we propose a novel drone detection framework, which leverages specific patterns in video traffic transmitted by drones. The patterns consist of repetitive synchronization packets (we call pivots), which we use as features for a machine learning classifier. We show that our framework can achieve up to 99% in detection accuracy over an encrypted WiFi channel using only 170 packets originated from the drone within 820ms time period. Our framework is able to identify drone transmissions even among very similar WiFi transmissions (such as video streams originated from security cameras) as well as in noisy scenarios with background traffic. Furthermore, the design of our pivot features enables the classifier to detect unprofiled drones in which the classifier has never trained on and is refined using a novel feature selection strategy that selects the features that have the discriminative power of detecting new unprofiled drones.
نوع الوثيقة: article in journal/newspaper
اللغة: English
العلاقة: info:eu-repo/semantics/altIdentifier/wos/WOS:001002666900004; volume:7; issue:2; firstpage:1; lastpage:29; numberofpages:29; journal:ACM TRANSACTIONS ON CYBER-PHYSICAL SYSTEMS; https://hdl.handle.net/11577/3490301Test; info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-85160674037
DOI: 10.1145/3579999
الإتاحة: https://doi.org/10.1145/3579999Test
https://hdl.handle.net/11577/3490301Test
رقم الانضمام: edsbas.E0D31717
قاعدة البيانات: BASE