University of Oulu

M. Bagaa, T. Taleb, J. B. Bernabe and A. Skarmeta, "A Machine Learning Security Framework for Iot Systems," in IEEE Access, vol. 8, pp. 114066-114077, 2020, doi: 10.1109/ACCESS.2020.2996214

A machine learning security framework for IoT systems

Saved in:
Author: Bagaa, Miloud1; Taleb, Tarik1,2,3; Bernabe, Jorge Bernal4;
Organizations: 1Department of Communications and Networking, School of Electrical Engineering, Aalto University, 02150 Espoo, Finland
2Department of Computer and Information Security, Sejong University, Seoul 05006, South Korea
3Centre for Wireless Communications (CWC), University of Oulu, 90570 Oulu, Finland
4Department of Communications and Information Engineering, University of Murcia, 30001 Murcia, Spain
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 5.5 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe2020081460402
Language: English
Published: Institute of Electrical and Electronics Engineers, 2020
Publish Date: 2020-08-14
Description:

Abstract

Internet of Things security is attracting a growing attention from both academic and industry communities. Indeed, IoT devices are prone to various security attacks varying from Denial of Service (DoS) to network intrusion and data leakage. This paper presents a novel machine learning (ML) based security framework that automatically copes with the expanding security aspects related to IoT domain. This framework leverages both Software Defined Networking (SDN) and Network Function Virtualization (NFV) enablers for mitigating different threats. This AI framework combines monitoring agent and AI-based reaction agent that use ML-Models divided into network patterns analysis, along with anomaly-based intrusion detection in IoT systems. The framework exploits the supervised learning, distributed data mining system and neural network for achieving its goals. Experiments results demonstrate the efficiency of the proposed scheme. In particular, the distribution of the attacks using the data mining approach is highly successful in detecting the attacks with high performance and low cost. Regarding our anomaly-based intrusion detection system (IDS) for IoT, we have evaluated the experiment in a real Smart building scenario using one-class SVM. The detection accuracy of anomalies achieved 99.71%. A feasibility study is conducted to identify the current potential solutions to be adopted and to promote the research towards the open challenges.

see all

Series: IEEE access
ISSN: 2169-3536
ISSN-E: 2169-3536
ISSN-L: 2169-3536
Volume: 8
Pages: 114066 - 114077
DOI: 10.1109/ACCESS.2020.2996214
OADOI: https://oadoi.org/10.1109/ACCESS.2020.2996214
Type of Publication: A1 Journal article – refereed
Field of Science: 213 Electronic, automation and communications engineering, electronics
Subjects:
NFV
SDN
Funding: This work was supported in part by the European Research Project H2020 ANASTACIA under Grant GA 731558, in part by the H2020 INSPIRE-5Gplus Project under Grant GA 871808, in part by the AXA Postdoctoral Scholarship awarded by the AXA Research Fund (Cyber-SecIoT project), in part by the Academy of Finland 6Genesis Project under Grant 318927, and in part by the Academy of Finland CSN Project under Grant 311654.
Academy of Finland Grant Number: 318927
Detailed Information: 318927 (Academy of Finland Funding decision)
Copyright information: © The Authors 2020. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.
  https://creativecommons.org/licenses/by/4.0/