publication . Other literature type . Project deliverable . 2020

Machine Learning empowered intrusion detection using Honeypots' data v1

Dr Serafeim Moustakidis;
Open Access English
  • Published: 30 Jun 2020
  • Publisher: Zenodo
Abstract
This deliverable presents the overall development status of the Machine Learning Intrusion Detection (MLID) component on M18 of the project’s lifetime and the end of the first interim of MLID’s two-staged development phases (M10-M18, M22-M30). This is a versioned document and describes the progress of the development of the first prototype of the component. Within the first development phase of MLID, feature exploration has been performed and a list of the most informative features (reflecting different aspects of users’ behaviour) has been identified. Three AI pipelines for intrusion detection have been designed, developed and evaluated in an extensive comparative analysis that includes multiple variants of each pipeline with numerous machine leaning (ML) and deep learning (DL) models.
Subjects
free text keywords: Machine learning, deep learning, intrusion detection
Funded by
EC| SPHINX
Project
SPHINX
A Universal Cyber Security Toolkit for Health-Care Industry
  • Funder: European Commission (EC)
  • Project Code: 826183
  • Funding stream: H2020 | RIA
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Open Access
ZENODO
Other literature type . 2020
Providers: ZENODO
Open Access
https://doi.org/10.5281/zenodo...
Project deliverable . 2020
Providers: Datacite
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