Skip to Main content Skip to Navigation
Journal articles

A modified LOF based approach for outlier characterization in IoT

Lynda Boukela 1 Gongxuan Zhang 1 Meziane Yacoub 2 Samia Bouzefrane 3 Sajjad Bagheri 1 Hamed Jelodar 1
2 CEDRIC - MSDMA - CEDRIC. Méthodes statistiques de data-mining et apprentissage
CEDRIC - Centre d'études et de recherche en informatique et communications
3 CEDRIC - ROC - CEDRIC. Réseaux et Objets Connectés
CEDRIC - Centre d'études et de recherche en informatique et communications
Abstract : The Internet of Things (IoT) is a growing paradigm that is revolutionary for Information and Communication Technology (ICT) because it gathers numerous application domains by integrating several enabling technologies. Outlier detection is a field of tremendous importance, including in IoT. In previous works on outlier detection, the proposed methods mainly tackled the efficacy and the efficiency challenges. However, a growing interest in the interpretation of the detected anomalies has been noticed by the research community, and some works have already contributed in this direction. Furthermore, characterizing anomalous events in IoT-related problems has not been conducted. Hence, in this paper, we introduce our modified Local Outlier Factor (LOF)-based outlier characterization approach and apply it to enhance the IoT security and reliability. Experiments on both synthetic and real-world datasets show the good performance of our solution.
Complete list of metadata

https://hal.archives-ouvertes.fr/hal-03381644
Contributor : Samia Bouzefrane Connect in order to contact the contributor
Submitted on : Sunday, October 17, 2021 - 4:15:19 PM
Last modification on : Thursday, November 18, 2021 - 10:57:31 AM

File

Article_Boukela.pdf
Files produced by the author(s)

Identifiers

Collections

Citation

Lynda Boukela, Gongxuan Zhang, Meziane Yacoub, Samia Bouzefrane, Sajjad Bagheri, et al.. A modified LOF based approach for outlier characterization in IoT. Annals of Telecommunications - annales des télécommunications, Springer, 2020, 76 (3-4), pp.145-153. ⟨10.1007/s12243-020-00780-5⟩. ⟨hal-03381644⟩

Share

Metrics

Record views

18

Files downloads

12