Outliers detection in network services with self-learned profiles

J. Henriques, L. Bernardo, R. Oliveira, P. Amaral, F. Ganhão, P. Pinto, R. Dinis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Wireless communication networks and services suffer from multiple kinds of security attacks which cannot be handled only at the wireless protocol level. This paper proposes an intrusion detection system that self-learns the user profiles using machine learning techniques. The system applies knowledge discovery techniques to generate a compact user profile offline. The profile is used to detect intrusions offline and online. Security breaches and ongoing attacks are identified detecting outlier activities in relation to the user profile and to immediate forecast behaviour. The later one provides a very fast warning, which is validated by the slower and more precise profile based online system. They are complemented by the slowest offline system, which is capable of maintaining updated user profiles. The system was implement using RStudio, and was tested using the 2014 Dendalion big data challenge dataset publicly available. The results show that the offline system has an outlier detection accuracy above 99% and that the online system was able to distinguish outlier activity from the users' own activity.

Original languageEnglish
Title of host publication2017 9th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops, ICUMT 2017
PublisherIEEE Computer Society
Pages238-243
Number of pages6
ISBN (Electronic)978-1-5386-3435-6
ISBN (Print)978-1-5386-3436-3
DOIs
Publication statusPublished - 11 Jan 2018
Event9th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops, ICUMT 2017 - Munich, Germany
Duration: 6 Nov 20178 Nov 2017

Publication series

Name International Conference on Ultra Modern Telecommunications and Control Systems & Workshops
PublisherIEEE Computer Society
Volume2017-November
ISSN (Print)2157-0221

Conference

Conference9th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops, ICUMT 2017
CountryGermany
CityMunich
Period6/11/178/11/17

Keywords

  • Big Data
  • Data mining
  • Network security
  • Outliers Detection
  • Supervised Classification
  • User Profile
  • Wireless Networks

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  • Cite this

    Henriques, J., Bernardo, L., Oliveira, R., Amaral, P., Ganhão, F., Pinto, P., & Dinis, R. (2018). Outliers detection in network services with self-learned profiles. In 2017 9th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops, ICUMT 2017 (pp. 238-243). ( International Conference on Ultra Modern Telecommunications and Control Systems & Workshops; Vol. 2017-November). IEEE Computer Society. https://doi.org/10.1109/ICUMT.2017.8255153