Detecting targeted interference in the Internet of Things

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dc.contributor.advisorRoedig, Utz
dc.contributor.advisorPesch, Dirk H J
dc.contributor.authorMorillo, Gabriela
dc.contributor.funderScience Foundation Irelanden
dc.date.accessioned2024-09-18T13:19:52Z
dc.date.available2024-09-18T13:19:52Z
dc.date.issued2024
dc.date.submitted2024
dc.description.abstractThis thesis investigates targeted jamming interference detection to enhance security in the Internet of Things (IoT) infrastructures. The study starts by assessing the critical role of IoT system monitoring in securing large networks, emphasising the need for automated solutions to detect and mitigate threats, ensuring continuous and reliable operations. This provided insight into how interference monitoring solutions should be implemented. The development of this kind of detector is important as naturally occurring interference requires a different response than targeted interference attacks. A significant portion of the thesis is dedicated to addressing vulnerabilities in the Narrowband-Internet of Things (NB-IoT), a Low Power Wide Area Network (LPWAN) radio technology required for large-scale IoT deployments. Initially, it looks specifically into how interference with NB-IoT synchronisation signals can lead to Denial of Service (DoS) attacks, highlighting the need to prevent and mitigate such vulnerabilities. A novel attack on the initial communication steps is provided in this investigation. To address these challenges, this work introduces a novel method for detecting targeted interference at the User Equipment (UE) level in NB-IoT networks. Our solution utilises network performance data and subframe loss rates to differentiate between targeted attacks and naturally occurring interference, which is critical as they require different responses. The costs associated with designing dedicated detectors for each technology, including established and upcoming ones, are high. Therefore, we propose a technology-independent approach to detect targeted interference across various IoT networks. This solution, designed to function on resource-constrained IoT devices, analyses packet loss rates and patterns to detect the presence of targeted attacks. This detection technique has been proven through comprehensive assessments using several IoT technologies, including NB-IoT and IEEE 802.15.4 GTS, demonstrating its effectiveness in distinguishing targeted interference from natural interference. This work advances the state of the art in detecting malicious interference in IoT environments by introducing a technology-independent targeted interference detection method capable of operating on resource-constrained IoT devices. Unlike prior research, which has primarily focused on machine learning IDS or including additional hardware for their solutions, our approach monitors packet loss rates and patterns across different wireless communication technologies (e.g. Narrowband Internet of Things and IEEE 802.15.4) to perform statistical anomaly detection. This is the first research to propose and validate a comprehensive, technology-independent framework that effectively distinguishes between targeted attacks and natural interference, thereby significantly enhancing the security and resilience of heterogeneous IoT deployments. Overall, our research emphasises the importance of robust monitoring systems and innovative defence mechanisms to safeguard IoT infrastructures against evolving and emerging threats while also contributing valuable insights and tools to enhance the resilience of critical IoT applications.en
dc.description.statusNot peer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationMorillo Fuetala, D. G. 2024. Detecting targeted interference in the Internet of Things. PhD Thesis, University College Cork.
dc.identifier.endpage197
dc.identifier.urihttps://hdl.handle.net/10468/16391
dc.language.isoenen
dc.publisherUniversity College Corken
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Centres for Research Training Programme::Data and ICT Skills for the Future/18/CRT/6222/IE/SFI Centre for Research Training in Advanced Networks for Sustainable Societies/
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Research Centres/13/RC/2077/IE/CONNECT: The Centre for Future Networks & Communications/
dc.rights© 2024, Diana Gabriela Morillo Fueltala.
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectInternet of Things
dc.subjectSecurity
dc.subjectAnomaly detection
dc.subjectJamming
dc.subjectNarrowband Internet of Things
dc.subjectIEEE 802.15.4
dc.subjectWireless IoT
dc.subjectTargeted interference
dc.subjectTechnology-independent detector
dc.titleDetecting targeted interference in the Internet of Things
dc.typeDoctoral thesisen
dc.type.qualificationlevelDoctoralen
dc.type.qualificationnamePhD - Doctor of Philosophyen
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