Downloads provided by UsageCounts
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.
Machine learning, deep learning, intrusion detection
Machine learning, deep learning, intrusion detection
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
| views | 12 | |
| downloads | 8 |

Views provided by UsageCounts
Downloads provided by UsageCounts