DC12 Project: Sustainable Real-time Anomaly Detection for Practical Applications

PhD Supervisor: Prof. Jia-Chun Lin (NTNU); co-supervisor: Sandeep Pirbhulal (NRS); Auxiliary supervisors: Victor Rodriguez-Fernandez (UPM); Volker Stolz (HVL), Dariusz Mrozek (SUT); R&D cooperation: AIUT

Objectives: Real-time anomaly detection has gained substantial attention in various fields due to its ability to provide immediate insights into data streams, enabling timely responses to emerging threats. It is invaluable in sectors such as cybersecurity, industrial monitoring, healthcare, finance, and smart infrastructure, where rapid anomaly identification can enhance security, efficiency, and decision-making processes. The challenge is how to make real-time anomaly detection sustainable, i.e., how to achieve the desired goals of real-time anomaly detection while assuring resource efficiency, scalability, data privacy, resilience, transparency, interpretability, and applicability across different safety-critical domains. The objective of this PhD project is to address the above-mentioned challenges.

Expected Results: By the end of this project, the following results are anticipated: (i) Comprehensive analysis of state-of-the-art solutions in real-time anomaly detection, with a focus on their sustainability aspects,; (ii) Successful development and implementation of a sustainable real-time anomaly detection solution tailored to effectively address the challenges identified earlier; (iii) Demonstration of the solution's applicability to safety-critical cyber-physical systems, showcasing its robustness in critical operational environments; (iv) Extensive evaluation of the implemented solution using a set of sustainability metrics, and (v) Dissemination of project outcomes through research papers and conference presentations.  

Applied research: The research result of this PhD project will be applied to the cyber-physical electricity systems in NRS’s the National Smart Grid Laboratory.  DC12 will also participate in AIUT’s applied projects on smart manufacturing.

Planned secondments: UPM(4 months); HVL(4 months); SUT (4 months)                   

Enrolment in Doctoral degree: NTNU