Detecting Attacks, Intrusions and Anomalies in Smart Grids
The power grid infrastructure is experiencing a dramatic change in the way it produces, distributes, and stores electricity. With these advancements, however, a new set of threats are also being enabled. In order to defend the smart grid infrastructure against novel attacks, new mechanisms for discovering threats must be developed. Fortunately, there is plenty of new information collected by intelligent sensors which can be leveraged to create mechanisms to detect attacks, intrusions and anomalies in smart grids.
With the addition of intelligent sensing devices, known as smart meters, information about usage patterns in the smart grid is being collected. This thesis project aims at developing intrusion detection techniques that can model normal usage patterns and detect deviations from these models. The developed techniques will rely on different machine learning algorithms and statistical analysis.
In order to evaluate methodologies for detecting threats in smart grids, we will provide real-world data related to the production and consumption of electricity, gas and heat in a real smart grid. Different machine learning algorithms need to be tested and evaluated on top of this data. Software is also expected to be developed where the proposed methodologies are demonstrated.
- Evaluate machine learning algorithms on top of data collected from a smart grid.
- Develop anomaly detection mechanisms.
- Perform feature engineering on the provided data.
- Implement software to demonstrate different machine learning methodologies.
- Basic understanding of supervised and unsupervised machine learning techniques
- Scripting and programming
- Familiarity with the concepts of anomaly detection
- Bonus: comfortable using GNU/Linux
- Bonus: Probability and statistics
- Carlos Garcia C. (carlos.garcia(a-t)tk.informatik.tu-darmstadt.de)
Forschungsgebiete:Telecooperation , Security, Security, Usability and Society