A. Presekal

A. Presekal

Profiel

Alfan Presekal

Biografie

Alfan Presekal is a PhD candidate of Intelligent Electrical Power Grids in the Faculty of Electrical Engineering, Mathematics and Computer Science at TU Delft. He was a lecturer in Computer Engineering, Department of Electrical Engineering, Universitas Indonesia from 2016-2019. Alfan got his Master Degree in Computing with the specialty in Secure Software System from Imperial College London in 2016. He got his Bachelor Degree in Computer Engineering Universitas Indonesia in 2014. He also participated in an exchange research program in Department Information and Communication Engineering, Tokyo Institute of Technology from 2012-2013. 

Projecten

Cyber Resiliency of Power Grid Operational Technologies

Integration of digital technologies and adoption of latest power system automation and communication standards are spearheading the power system transition towards a smart grid. Cyber security is an emerging topic as Information and Communication Technology (ICT) – Operational Technology (OT) connectivity of power grids is rapidly increasing. Digitalisation of power grids may not be possible without the consideration of cyber security. ICT and OT have vulnerabilities that can be exploited in a cyber attack on smart grids. The recent cyber attacks on Ukrainian power grid in 2015 and 2016 have revealed that power grids are vulnerable to cyber intrusions, which can lead to power outages or even a blackout.

The PhD project aims to improve cyber security and resilience of power grids to cyber attacks by proposing a novel intrusion detection and prevention system. To achieve this objective, the research is divided into three parts:

  1. Model the Cyber-Physical power System (CPS). CPS co-simulation models are required to generate power system data and OT communication traffic in real-time, model and simulate cyber attacks, analyse their impact on power system operation, and develop mitigation solutions.
  2. Develop an Intrusion Detection System (IDS). The proposed IDS is using deep learning to detect anomalies in communication network traffic. CPS data is needed for training the deep learning algorithms.
  3. Develop an Intrusion Prevention System (IPS). IPS has traffic blocking capabilities. It coordinates with IDS and next generation firewalls to mitigate the cyber attacks. 
The results of this PhD research will be demonstrated in the Control Room of the Future at TU Delft.


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