A data infrastructure for heterogeneous telemetry adaptation: application to Netflow-based cryptojacking detection

The increasing development of cryptocurrencies has brought cryptojacking as a new security threat in which attackers steal computing resources for cryptomining. The digitization of the supply chain is a potential major target for cryptojacking due to the large number of different infrastructures inv...

Full description

Saved in:
Bibliographic Details
Published in:Annales des télécommunications 2024-04, Vol.79 (3-4), p.241-256
Main Authors: Moreno-Sancho, Alejandro A., Pastor, Antonio, Martinez-Casanueva, Ignacio D., González-Sánchez, Daniel, Triana, Luis Bellido
Format: Article
Language:English
Subjects:
Circuits
Communications Engineering
Communications traffic
Computer Communication Networks
Cryptocurrency mining
Data transmission
Digital currencies
Engineering
Information and Communication
Information sources
Information Systems and Communication Service
Machine learning
Networks
R & D/Technology Policy
Signal,Image and Speech Processing
Supply chains
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The increasing development of cryptocurrencies has brought cryptojacking as a new security threat in which attackers steal computing resources for cryptomining. The digitization of the supply chain is a potential major target for cryptojacking due to the large number of different infrastructures involved. These different infrastructures provide information sources that can be useful to detect cryptojacking, but with a wide variety of data formats and encodings. This paper describes the semantic data aggregator (SDA), a normalization and aggregation system based on data modelling and low-latency processing of data streams that facilitates the integration of heterogeneous information sources. As a use case, the paper describes a cryptomining detection system (CDS) based on network traffic flows processed by a machine learning engine. The results show how the SDA is leveraged in this use case to obtain aggregated information that improves the performance of the CDS.
ISSN:0003-4347
1958-9395
DOI:10.1007/s12243-023-00991-6