Multi-Defender Strategic Filtering Against Multi Agent Cyber Epidemics on Multi-Environment Model for Smart Grid Protection
The growing cyber space with the developments in cyber network technologies in smart grid (SG) systems has necessitated questioning the reliability of networks and taking precautions against possible cyber threats. For this reason, defensive strategies and approaches against cyber attacks must be im...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2023-01-01
|
| Series: | E3S Web of Conferences |
| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/106/e3sconf_icegc2023_00095.pdf |
| _version_ | 1797344942226407424 |
|---|---|
| author | Bitirgen Kübra Başaran Filik Ümmühan |
| author_facet | Bitirgen Kübra Başaran Filik Ümmühan |
| author_sort | Bitirgen Kübra |
| collection | DOAJ |
| description | The growing cyber space with the developments in cyber network technologies in smart grid (SG) systems has necessitated questioning the reliability of networks and taking precautions against possible cyber threats. For this reason, defensive strategies and approaches against cyber attacks must be improved to sustain secure information flow of the network connections used in electricity generation, transmission, distribution, and consumption. This paper proposes a multi-agent multi environment deep reinforcement learning (MM-DRL) based defender response against cyber epidemics consisting coordinated cyber-attacks (multi-CAs) in the same time frame scheme to sustain security for SG networks. In this regard, the PMU-connected 123-bus system is integrated as a Markov game. MM-DRL approach is implemented for subenvironments of a typical SG system. Multi-CAs game aims to coordinate PMU signals across intersections to improve the network efficiency of a SG. DRL has been applied to data control recently and demonstrated promising performance where each data signal is regarded as an agent. Conversely, multi-CAs are self-renewing emerging causative agent of electricity theft, network disturbances, and data manipulation in SG systems characterized with wide characteristic diversity and rapid evolution. The game results show that the presented request response algorithm is able to minimize system attack damage and maintain protection duties when compared to a benchmark without request response. In addition, the performance of the MM-DRL approach compared to other developed methods is examined. |
| first_indexed | 2024-03-08T11:10:15Z |
| format | Article |
| id | doaj.art-40bfda263091430491523c9b88667aa3 |
| institution | Directory Open Access Journal |
| issn | 2267-1242 |
| language | English |
| last_indexed | 2024-03-08T11:10:15Z |
| publishDate | 2023-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj.art-40bfda263091430491523c9b88667aa32024-01-26T10:45:10ZengEDP SciencesE3S Web of Conferences2267-12422023-01-014690009510.1051/e3sconf/202346900095e3sconf_icegc2023_00095Multi-Defender Strategic Filtering Against Multi Agent Cyber Epidemics on Multi-Environment Model for Smart Grid ProtectionBitirgen Kübra0Başaran Filik Ümmühan1Department of Electronics & Communication Technology, Army NCO Vocational HE School, National Defence UniversityDepartment of Electrical and Electronics Engineering, Eskişehir Technical UniversityThe growing cyber space with the developments in cyber network technologies in smart grid (SG) systems has necessitated questioning the reliability of networks and taking precautions against possible cyber threats. For this reason, defensive strategies and approaches against cyber attacks must be improved to sustain secure information flow of the network connections used in electricity generation, transmission, distribution, and consumption. This paper proposes a multi-agent multi environment deep reinforcement learning (MM-DRL) based defender response against cyber epidemics consisting coordinated cyber-attacks (multi-CAs) in the same time frame scheme to sustain security for SG networks. In this regard, the PMU-connected 123-bus system is integrated as a Markov game. MM-DRL approach is implemented for subenvironments of a typical SG system. Multi-CAs game aims to coordinate PMU signals across intersections to improve the network efficiency of a SG. DRL has been applied to data control recently and demonstrated promising performance where each data signal is regarded as an agent. Conversely, multi-CAs are self-renewing emerging causative agent of electricity theft, network disturbances, and data manipulation in SG systems characterized with wide characteristic diversity and rapid evolution. The game results show that the presented request response algorithm is able to minimize system attack damage and maintain protection duties when compared to a benchmark without request response. In addition, the performance of the MM-DRL approach compared to other developed methods is examined.https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/106/e3sconf_icegc2023_00095.pdf |
| spellingShingle | Bitirgen Kübra Başaran Filik Ümmühan Multi-Defender Strategic Filtering Against Multi Agent Cyber Epidemics on Multi-Environment Model for Smart Grid Protection E3S Web of Conferences |
| title | Multi-Defender Strategic Filtering Against Multi Agent Cyber Epidemics on Multi-Environment Model for Smart Grid Protection |
| title_full | Multi-Defender Strategic Filtering Against Multi Agent Cyber Epidemics on Multi-Environment Model for Smart Grid Protection |
| title_fullStr | Multi-Defender Strategic Filtering Against Multi Agent Cyber Epidemics on Multi-Environment Model for Smart Grid Protection |
| title_full_unstemmed | Multi-Defender Strategic Filtering Against Multi Agent Cyber Epidemics on Multi-Environment Model for Smart Grid Protection |
| title_short | Multi-Defender Strategic Filtering Against Multi Agent Cyber Epidemics on Multi-Environment Model for Smart Grid Protection |
| title_sort | multi defender strategic filtering against multi agent cyber epidemics on multi environment model for smart grid protection |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/106/e3sconf_icegc2023_00095.pdf |
| work_keys_str_mv | AT bitirgenkubra multidefenderstrategicfilteringagainstmultiagentcyberepidemicsonmultienvironmentmodelforsmartgridprotection AT basaranfilikummuhan multidefenderstrategicfilteringagainstmultiagentcyberepidemicsonmultienvironmentmodelforsmartgridprotection |