A Machine Learning Security Framework for Iot Systems

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openAccess
Journal Title
Journal ISSN
Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Date
2020-01-01
Major/Subject
Mcode
Degree programme
Language
en
Pages
12
114066-114077
Series
IEEE Access, Volume 8
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.
Description
| openaire: EC/H2020/731558/EU//ANASTACIA | openaire: EC/H2020/871808/EU//INSPIRE-5Gplus
Keywords
artificial intelligence, Internet of Things, NFV, orchestration and MANO, SDN, security
Other note
Citation
Bagaa, M, Taleb, T, Bernabe, J B & Skarmeta, A 2020, ' A Machine Learning Security Framework for Iot Systems ', IEEE Access, vol. 8, 9097876, pp. 114066-114077 . https://doi.org/10.1109/ACCESS.2020.2996214