| Autor: |
null Ch. Srinivas Goutham, null Bevera Nikhil, null Chammallamudi Sritrivedhi, null Ch Sudhir Kumar, null L Sankara Rao |
| Rok vydání: |
2023 |
| Předmět: |
|
| Zdroj: |
International Journal of Advanced Research in Science, Communication and Technology. :71-83 |
| ISSN: |
2581-9429 |
| Popis: |
Cyber-physical systems (CPS) that are enabled by the Internet of Things (IoT) can be difficult to protect because security measures designed for general information/operational technology (IT/OT) systems may not be as effective in a CPS setting. As a result, an industrial control system (ICS)-specific, two-level ensemble attack detection and attribution framework is presented in this article. A decision tree and a novel ensemble deep representation-learning model are used to detect attacks in imbalanced ICS environments at the first level. An ensemble deep neural network is made to make attack attribution easier at the second level. Gas pipeline and water treatment system data sets are used to evaluate the proposed model. The results show that, despite having a similar computational complexity, the proposed model performs better than other competing methods |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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