Co-funded by the European Commission under Horizon 2020 programme, SIMARGL is a project that provides innovative advanced solutions to effectively fight the pressing problem of malware. Its name stands for “Secure Intelligent Methods for Advanced RecoGnition of malware and stegomalware”.


The project aims to tackle the new challenges in the cybersecurity field, including information hiding methods, network anomalies, stegomalware, ransomware, mobile malware and other malicious activities in computer networks and applications.

For this goal, SIMARGL offers an integrated and validated toolkit improving European cybersecurity. The toolkit uses innovative machine and deep learning methods do detect malware (including stegomalware), ransomware and network anomalies.

SIMARGL deploys breakthrough methods and algorithms to analyze the data from networks, such as: concept drift detectors, advanced signal processing and transformations, lifelong learning intelligent systems (LLIS) approach, hybrid classifiers, and deep learning, just to mention some techniques.

14 partners from 7 European countries unite their expertise and know-how:

  • Coordinator: FernUniversität in Hagen, Germany
  • Software Imagination & Vision (SIMAVI) from Romania
  • Netzfactor GmbH from Germany
  • Airbus CyberSecurity SAS from France
  • Thales SIX GTS France from France
  • Consiglio Nazionale delle Ricerche from Italy
  • NUMERA S.p.a. from Italy
  • Pluribus-One from Italy
  • Institute of International Relations from Czechia
  • ITTI Sp. z o.o. from Poland
  • Warsaw University of Technology from Poland
  • CERT Orange Polska from Poland
  • RoEduNet (ARNIEC Agency) from Romania
  • Stichting CUIng Foundation from the Netherlands

SIMAVI, in charge the integration of the overall solution

The Romanian integrator will integrate and test the overall solution in a lab environment deployment. Together with RoEduNet (ARNIEC Agency) from Romania, SIMAVI is in charge of planning the evaluation during the trial pilots for quantifying expected results. The teams have already set‐up the lab environment as well as the actual pilot environment and they will execute the pilots and evaluate the final technical solution.

SIMAVI used different technologies in this project, such as Apache Kafka, Apache Flink and Elasticsearch, and Java as the preferred programming language.

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 833042 and will end in April 2022.

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