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Hacking your drone data
Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
2021 (engelsk)Independent thesis Advanced level (degree of Master (One Year)), 10 poäng / 15 hpOppgave
Abstract [en]

The last couple of years has seen a rapid growth in smart devices. The smart devices are exponentially gaining more popularity both as a complement to our daily lives in the form of IoT products aiding in our everyday tasks and as a way we communicate and work. An estimation of 75.44 billion devices will be connected to the internet by 2025. With the rapid development and normalization of IoT devices, questions regarding privacy has never been more important. This thesis focuses on privacy in relation to one of the most emerging technologies, drones. 

Drones have been discussed frequently in both governmental and commercial sectors for its inevitable normalization in the airspace. Previously privacy and drones has been researched and discussed from the point of view of which drones are used to infringe on people’s privacy. This thesis explores privacy from another point of view, the view of the drone owner. By exploring privacy from the drone users’ point of view, this thesis shows the importance of better privacy measurements by proposing a conceptual model to existing popular privacy definitions. To investigate privacy in this context, a case has been conducted which proved and validated what kind of data is at risk of being hijacked. The thesis provides a conceptual model that aims to help commercial drone owners to analyze how privacy infringements can occur, why they could occur and how to account for them in the future. Furthermore, the thesis highlights the vulnerability that WIFI dependent devices poses with DDoS attacks. The findings of this thesis show that an infringement of privacy regarding commercial drones requires more clear privacy regulations and definitions, as well as highlighting privacy vulnerabilities in commercial drones. 

sted, utgiver, år, opplag, sider
2021. , s. 76
HSV kategori
Identifikatorer
URN: urn:nbn:se:mau:diva-41403OAI: oai:DiVA.org:mau-41403DiVA, id: diva2:1539336
Utdanningsprogram
TS Computer Science, Master Programme
Veileder
Examiner
Tilgjengelig fra: 2021-03-23 Laget: 2021-03-23 Sist oppdatert: 2021-03-23bibliografisk kontrollert

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