A Bayesian Deep Learning Approach With Convolutional Feature Engineering to Discriminate Cyber-Physical Intrusions in Smart Grid Systems
<p dir="ltr">The emergence of cyber-physical smart grid (CPSG) systems has revolutionized the traditional power grid by enabling the bidirectional energy flow between consumers and utilities. However, due to escalated information exchange between the end-users, it has posed a greater...
محفوظ في:
| المؤلف الرئيسي: | |
|---|---|
| مؤلفون آخرون: | , , , , |
| منشور في: |
2023
|
| الموضوعات: | |
| الوسوم: |
إضافة وسم
لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!
|
| _version_ | 1864513527580983296 |
|---|---|
| author | Devinder Kaur (264278) |
| author2 | Adnan Anwar (9644240) Innocent Kamwa (12757145) Shama Islam (15801500) S. M. Muyeen (14778337) Nasser Hosseinzadeh (15803285) |
| author2_role | author author author author author |
| author_facet | Devinder Kaur (264278) Adnan Anwar (9644240) Innocent Kamwa (12757145) Shama Islam (15801500) S. M. Muyeen (14778337) Nasser Hosseinzadeh (15803285) |
| author_role | author |
| dc.creator.none.fl_str_mv | Devinder Kaur (264278) Adnan Anwar (9644240) Innocent Kamwa (12757145) Shama Islam (15801500) S. M. Muyeen (14778337) Nasser Hosseinzadeh (15803285) |
| dc.date.none.fl_str_mv | 2023-02-22T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/access.2023.3247947 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/A_Bayesian_Deep_Learning_Approach_With_Convolutional_Feature_Engineering_to_Discriminate_Cyber-Physical_Intrusions_in_Smart_Grid_Systems/25204196 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Electrical engineering Electronics, sensors and digital hardware Materials engineering Feature extraction Convolutional neural networks Bayes methods Uncertainty Probabilistic logic Neural networks Classification algorithms Deep learning Intrusion detection Smart grids SCADA systems |
| dc.title.none.fl_str_mv | A Bayesian Deep Learning Approach With Convolutional Feature Engineering to Discriminate Cyber-Physical Intrusions in Smart Grid Systems |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">The emergence of cyber-physical smart grid (CPSG) systems has revolutionized the traditional power grid by enabling the bidirectional energy flow between consumers and utilities. However, due to escalated information exchange between the end-users, it has posed a greater challenge to the cyber security mechanisms for the communication networks at the cyber and physical planes. To address these challenges, we propose a Bayesian approach integrated with deep convolutional neural networks (CNN-Bayesian). While, the Bayesian component is used to discriminate cyber-physical intrusions from the normal events in the binary and multi-class events. CNN layers are utilized to handle the high-dimensional feature space prior to the intrusions classification task. The proposed method is validated using real-time Industrial control systems (ICS) dataset against the standard deep learning-based classification methods such as recurrent neural networks (RNN) and long-short term memory (LSTM). From the experimental results, it can be inferred that the proposed CNN-Bayesian method outperforms the existing benchmark classification methods to discriminate intrusions in CPSG systems using evaluation metrics such as accuracy, precision, recall, and F1 -score.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="http://creativecommons.org/licenses/by/4.0" target="_blank">http://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2023.3247947" target="_blank">https://dx.doi.org/10.1109/access.2023.3247947</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_c1d65dab44be921e02c2ad47bb72ed7e |
| identifier_str_mv | 10.1109/access.2023.3247947 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/25204196 |
| publishDate | 2023 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | A Bayesian Deep Learning Approach With Convolutional Feature Engineering to Discriminate Cyber-Physical Intrusions in Smart Grid SystemsDevinder Kaur (264278)Adnan Anwar (9644240)Innocent Kamwa (12757145)Shama Islam (15801500)S. M. Muyeen (14778337)Nasser Hosseinzadeh (15803285)EngineeringElectrical engineeringElectronics, sensors and digital hardwareMaterials engineeringFeature extractionConvolutional neural networksBayes methodsUncertaintyProbabilistic logicNeural networksClassification algorithmsDeep learningIntrusion detectionSmart gridsSCADA systems<p dir="ltr">The emergence of cyber-physical smart grid (CPSG) systems has revolutionized the traditional power grid by enabling the bidirectional energy flow between consumers and utilities. However, due to escalated information exchange between the end-users, it has posed a greater challenge to the cyber security mechanisms for the communication networks at the cyber and physical planes. To address these challenges, we propose a Bayesian approach integrated with deep convolutional neural networks (CNN-Bayesian). While, the Bayesian component is used to discriminate cyber-physical intrusions from the normal events in the binary and multi-class events. CNN layers are utilized to handle the high-dimensional feature space prior to the intrusions classification task. The proposed method is validated using real-time Industrial control systems (ICS) dataset against the standard deep learning-based classification methods such as recurrent neural networks (RNN) and long-short term memory (LSTM). From the experimental results, it can be inferred that the proposed CNN-Bayesian method outperforms the existing benchmark classification methods to discriminate intrusions in CPSG systems using evaluation metrics such as accuracy, precision, recall, and F1 -score.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="http://creativecommons.org/licenses/by/4.0" target="_blank">http://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2023.3247947" target="_blank">https://dx.doi.org/10.1109/access.2023.3247947</a></p>2023-02-22T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/access.2023.3247947https://figshare.com/articles/journal_contribution/A_Bayesian_Deep_Learning_Approach_With_Convolutional_Feature_Engineering_to_Discriminate_Cyber-Physical_Intrusions_in_Smart_Grid_Systems/25204196CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/252041962023-02-22T03:00:00Z |
| spellingShingle | A Bayesian Deep Learning Approach With Convolutional Feature Engineering to Discriminate Cyber-Physical Intrusions in Smart Grid Systems Devinder Kaur (264278) Engineering Electrical engineering Electronics, sensors and digital hardware Materials engineering Feature extraction Convolutional neural networks Bayes methods Uncertainty Probabilistic logic Neural networks Classification algorithms Deep learning Intrusion detection Smart grids SCADA systems |
| status_str | publishedVersion |
| title | A Bayesian Deep Learning Approach With Convolutional Feature Engineering to Discriminate Cyber-Physical Intrusions in Smart Grid Systems |
| title_full | A Bayesian Deep Learning Approach With Convolutional Feature Engineering to Discriminate Cyber-Physical Intrusions in Smart Grid Systems |
| title_fullStr | A Bayesian Deep Learning Approach With Convolutional Feature Engineering to Discriminate Cyber-Physical Intrusions in Smart Grid Systems |
| title_full_unstemmed | A Bayesian Deep Learning Approach With Convolutional Feature Engineering to Discriminate Cyber-Physical Intrusions in Smart Grid Systems |
| title_short | A Bayesian Deep Learning Approach With Convolutional Feature Engineering to Discriminate Cyber-Physical Intrusions in Smart Grid Systems |
| title_sort | A Bayesian Deep Learning Approach With Convolutional Feature Engineering to Discriminate Cyber-Physical Intrusions in Smart Grid Systems |
| topic | Engineering Electrical engineering Electronics, sensors and digital hardware Materials engineering Feature extraction Convolutional neural networks Bayes methods Uncertainty Probabilistic logic Neural networks Classification algorithms Deep learning Intrusion detection Smart grids SCADA systems |