SeRWas - Secure Resilient Water Management

Water supply and wastewater treatment plants are steadily transforming from traditional physical infrastructures to cyber-physical systems (CPS). Digitalization brings new vulnerabilities and attack surfaces for attacks from cyberspace. There has been an increase in reported cyberattacks on water management assets, demonstrating that working preventive measures are as necessary as early detection and location of attacked system components. Traditional mechanisms for detecting cyberattacks are becoming increasingly ineffective. At the same time, however, the use of new technologies is creating new legal and ethical risks. To address these new challenges, SeRWas will conduct research services towards a comprehensive cyber situational awareness solution for the water industry that already takes into account future legal, regulatory, and ethical requirements. Specifically, SeRWas will help the water industry to (1) reduce the attack surface for cyberattacks through methods and tools for detailed assessment and risk analysis, (2) counter the increasing sophistication of cyberattacks by developing advanced and robust algorithms as trusted artificial intelligence (AI) tools for early and ongoing attack detection and better situational and risk assessment; and (3) improve alignment with best practices and awareness of emerging security architectures through targeted innovative knowledge delivery methods.
Mitarbeiter*innen
Projektleiter/in an der OE
Dirk Muschalla
Univ.-Prof. Dr.-Ing.
Olga Saukh
bak. Assoc.Prof. Dr.rer.nat. MSc
Kontaktperson
Kay Uwe Römer
Univ.-Prof. Dipl.-Inform. Dr.sc.ETH
Teilnehmer*innen / Mitarbeiter*innen
Katarina Milenković
MSc
Fördergeber*innen
  • Österreichische Forschungsförderungsgesellschaft mbH (FFG) , FFG
Externe Partner
  • Bundesministerium für Land- und Forstwirtschaft, Regionen und Wasserwirtschaft, BML
  • JOANNEUM RESEARCH Forschungsgesellschaft mbH
Beginn: 31.10.2023

Publikationen

2024
Jakob Shack, Katarina Petrovic and Olga Saukh Breaking the Illusion: Real-world Challenges for Adversarial Patches in Object Detection Publikation in PURE anzeigen