Abstract
To minimize the effort required by human security operators in understanding and resolving attacks on the smart grid cyber-physical system, automated detection, prevention and mitigation tools have been integrated into the infrastructure. However, existing visualization frameworks at command and control centers present information from such tools in a nonintuitive, non-contextual format, reducing the situation awareness and timeliness of decisions. There is a need for frameworks that can contextualize the data in a human-understandable format prior to visualizing. To this end, the paper conducts a high-level review of existing literature, and introduces a conceptual human-on-the-loop framework of three modules: data analyzer comprising Kafka, Apache Spark and R, classifier comprising a deep neural network, and situation-aware decision-maker comprising a learning-based cognitive model. Preliminary proof of concept is shown for data analyzer by applying it to contextualize alerts from multiple photovoltaic systems in Florida.
Original language | English |
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Title of host publication | Southeastcon 2018 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
ISBN (Electronic) | 9781538661338 |
DOIs | |
State | Published - Oct 1 2018 |
Externally published | Yes |
Event | 2018 IEEE Southeastcon, Southeastcon 2018 - St. Petersburg, United States Duration: Apr 19 2018 → Apr 22 2018 |
Publication series
Name | Conference Proceedings - IEEE SOUTHEASTCON |
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Volume | 2018-April |
ISSN (Print) | 1091-0050 |
ISSN (Electronic) | 1558-058X |
Conference
Conference | 2018 IEEE Southeastcon, Southeastcon 2018 |
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Country/Territory | United States |
City | St. Petersburg |
Period | 04/19/18 → 04/22/18 |
Funding
The material published is a result of the research supported by the U.S. Department of Energy under the Award number DE-OE0000779.
Keywords
- Apache Spark
- cyber-physical security
- data processing
- human-on-the-loop
- situation awareness
- smart grid