| Title | Exploring Ensemble Classifiers for Detecting Attacks in the Smart Grids |
| Publication Type | Conference Paper |
| Year of Publication | 2018 |
| Authors | Kaur, Kudrat Jot, Hahn, Adam |
| Conference Name | Proceedings of the Fifth Cybersecurity Symposium |
| Publisher | ACM |
| Conference Location | New York, NY, USA |
| ISBN Number | 978-1-4503-6406-5 |
| Keywords | Intrusion detection, machine learning, Metrics, pubcrawl, Resiliency, Scalability, smart grid security |
| Abstract | The advent of machine learning has made it a popular tool in various areas. It has also been applied in network intrusion detection. However, machine learning hasn't been sufficiently explored in the cyberphysical domains such as smart grids. This is because a lot of factors weigh in while using these tools. This paper is about intrusion detection in smart grids and how some machine learning techniques can help achieve this goal. It considers the problems of feature and classifier selection along with other data ambiguities. The goal is to apply the machine learning ensemble classifiers on the smart grid traffic and evaluate if these methods can detect anomalies in the system. |
| URL | http://doi.acm.org/10.1145/3212687.3212873 |
| DOI | 10.1145/3212687.3212873 |
| Citation Key | kaur_exploring_2018 |
Exploring Ensemble Classifiers for Detecting Attacks in the Smart Grids