Intrusion detection using network traffic profiling and machine learning for IoT

Joseph Rose, Matthew Swann, Gueltoum Bendiab, Stavros Shiaeles, Nicholas Kolokotronis

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

25 Downloads (Pure)

Abstract

The rapid increase in the use of IoT devices brings many benefits to the digital society, ranging from improved efficiency to higher productivity. However, the limited resources and the open nature of these devices make them vulnerable to various cyber threats. A single compromised device can have an impact on the whole network and lead to major security and physical damages. This paper explores the potential of using network profiling and machine learning to secure IoT against cyber attacks. The proposed anomaly-based intrusion detection solution dynamically and actively profiles and monitors all networked devices for the detection of IoT device tampering attempts as well as suspicious network transactions. Any deviation from the defined profile is considered to be an attack and is subject to further analysis. Raw traffic is also passed on to the machine learning classifier for examination and identification of potential attacks. Performance assessment of the proposed methodology is conducted on the Cyber-Trust testbed using normal and malicious network traffic. The experimental results show that the proposed anomaly detection system delivers promising results with an overall accuracy of 98.35% and 0.98% of false-positive alarm
Original languageEnglish
Title of host publication2021 IEEE 7th International Conference on Network Softwarization (NetSoft)
PublisherIEEE
Pages409-415
ISBN (Electronic)9781665405225
ISBN (Print)9781665446358
DOIs
Publication statusPublished - 26 Jul 2021
EventIEEE 7th International Conference on Network Softwarization: NetSoft 2021 - Virtual, Tokyo, Japan
Duration: 28 Jun 20212 Jul 2021

Publication series

NameIEEE NetSoft Proceedings Series
PublisherIEEE
ISSN (Print)2693-9770
ISSN (Electronic)2693-9789

Conference

ConferenceIEEE 7th International Conference on Network Softwarization
Country/TerritoryJapan
CityTokyo
Period28/06/212/07/21

Keywords

  • Machine Learning
  • Intrusion Detection System
  • Security
  • Internet of Things
  • network profiling

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