VHDRA: A Vertical and Horizontal Intelligent Dataset Reduction Approach for Cyber-Physical Power Aware Intrusion Detection Systems
<p dir="ltr">The Cypher Physical Power Systems (CPPS) became vital targets for intruders because of the large volume of high speed heterogeneous data provided from the Wide Area Measurement Systems (WAMS). The Nonnested Generalized Exemplars (NNGE) algorithm is one of the most accura...
محفوظ في:
| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | |
| منشور في: |
2019
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| الموضوعات: | |
| الوسوم: |
إضافة وسم
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| _version_ | 1864513511908966400 |
|---|---|
| author | Hisham A. Kholidy (18891802) |
| author2 | Abdelkarim Erradi (13475740) |
| author2_role | author |
| author_facet | Hisham A. Kholidy (18891802) Abdelkarim Erradi (13475740) |
| author_role | author |
| dc.creator.none.fl_str_mv | Hisham A. Kholidy (18891802) Abdelkarim Erradi (13475740) |
| dc.date.none.fl_str_mv | 2019-06-01T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1155/2019/6816943 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/VHDRA_A_Vertical_and_Horizontal_Intelligent_Dataset_Reduction_Approach_for_Cyber-Physical_Power_Aware_Intrusion_Detection_Systems/26114602 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Information and computing sciences Cybersecurity and privacy Data management and data science Distributed computing and systems software Digital Twin Power system security Particle Swarm Optimization Machine learning Real-time data processing |
| dc.title.none.fl_str_mv | VHDRA: A Vertical and Horizontal Intelligent Dataset Reduction Approach for Cyber-Physical Power Aware Intrusion Detection Systems |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">The Cypher Physical Power Systems (CPPS) became vital targets for intruders because of the large volume of high speed heterogeneous data provided from the Wide Area Measurement Systems (WAMS). The Nonnested Generalized Exemplars (NNGE) algorithm is one of the most accurate classification techniques that can work with such data of CPPS. However, NNGE algorithm tends to produce rules that test a large number of input features. This poses some problems for the large volume data and hinders the scalability of any detection system. In this paper, we introduce VHDRA, a Vertical and Horizontal Data Reduction Approach, to improve the classification accuracy and speed of the NNGE algorithm and reduce the computational resource consumption. VHDRA provides the following functionalities: (1) it vertically reduces the dataset features by selecting the most significant features and by reducing the NNGE’s hyperrectangles. (2) It horizontally reduces the size of data while preserving original key events and patterns within the datasets using an approach called STEM, State Tracking and Extraction Method. The experiments show that the overall performance of VHDRA using both the vertical and the horizontal reduction reduces the NNGE hyperrectangles by 29.06%, 37.34%, and 26.76% and improves the accuracy of the NNGE by 8.57%, 4.19%, and 3.78% using the Multi-, Binary, and Triple class datasets, respectively.</p><h2>Other Information</h2><p dir="ltr">Published in: Security and Communication Networks<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/10.1155/2019/6816943" target="_blank">https://dx.doi.org/10.1155/2019/6816943</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_de1e7219b196852524f54b65894452ea |
| identifier_str_mv | 10.1155/2019/6816943 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/26114602 |
| publishDate | 2019 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | VHDRA: A Vertical and Horizontal Intelligent Dataset Reduction Approach for Cyber-Physical Power Aware Intrusion Detection SystemsHisham A. Kholidy (18891802)Abdelkarim Erradi (13475740)Information and computing sciencesCybersecurity and privacyData management and data scienceDistributed computing and systems softwareDigital TwinPower system securityParticle Swarm OptimizationMachine learningReal-time data processing<p dir="ltr">The Cypher Physical Power Systems (CPPS) became vital targets for intruders because of the large volume of high speed heterogeneous data provided from the Wide Area Measurement Systems (WAMS). The Nonnested Generalized Exemplars (NNGE) algorithm is one of the most accurate classification techniques that can work with such data of CPPS. However, NNGE algorithm tends to produce rules that test a large number of input features. This poses some problems for the large volume data and hinders the scalability of any detection system. In this paper, we introduce VHDRA, a Vertical and Horizontal Data Reduction Approach, to improve the classification accuracy and speed of the NNGE algorithm and reduce the computational resource consumption. VHDRA provides the following functionalities: (1) it vertically reduces the dataset features by selecting the most significant features and by reducing the NNGE’s hyperrectangles. (2) It horizontally reduces the size of data while preserving original key events and patterns within the datasets using an approach called STEM, State Tracking and Extraction Method. The experiments show that the overall performance of VHDRA using both the vertical and the horizontal reduction reduces the NNGE hyperrectangles by 29.06%, 37.34%, and 26.76% and improves the accuracy of the NNGE by 8.57%, 4.19%, and 3.78% using the Multi-, Binary, and Triple class datasets, respectively.</p><h2>Other Information</h2><p dir="ltr">Published in: Security and Communication Networks<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/10.1155/2019/6816943" target="_blank">https://dx.doi.org/10.1155/2019/6816943</a></p>2019-06-01T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1155/2019/6816943https://figshare.com/articles/journal_contribution/VHDRA_A_Vertical_and_Horizontal_Intelligent_Dataset_Reduction_Approach_for_Cyber-Physical_Power_Aware_Intrusion_Detection_Systems/26114602CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/261146022019-06-01T00:00:00Z |
| spellingShingle | VHDRA: A Vertical and Horizontal Intelligent Dataset Reduction Approach for Cyber-Physical Power Aware Intrusion Detection Systems Hisham A. Kholidy (18891802) Information and computing sciences Cybersecurity and privacy Data management and data science Distributed computing and systems software Digital Twin Power system security Particle Swarm Optimization Machine learning Real-time data processing |
| status_str | publishedVersion |
| title | VHDRA: A Vertical and Horizontal Intelligent Dataset Reduction Approach for Cyber-Physical Power Aware Intrusion Detection Systems |
| title_full | VHDRA: A Vertical and Horizontal Intelligent Dataset Reduction Approach for Cyber-Physical Power Aware Intrusion Detection Systems |
| title_fullStr | VHDRA: A Vertical and Horizontal Intelligent Dataset Reduction Approach for Cyber-Physical Power Aware Intrusion Detection Systems |
| title_full_unstemmed | VHDRA: A Vertical and Horizontal Intelligent Dataset Reduction Approach for Cyber-Physical Power Aware Intrusion Detection Systems |
| title_short | VHDRA: A Vertical and Horizontal Intelligent Dataset Reduction Approach for Cyber-Physical Power Aware Intrusion Detection Systems |
| title_sort | VHDRA: A Vertical and Horizontal Intelligent Dataset Reduction Approach for Cyber-Physical Power Aware Intrusion Detection Systems |
| topic | Information and computing sciences Cybersecurity and privacy Data management and data science Distributed computing and systems software Digital Twin Power system security Particle Swarm Optimization Machine learning Real-time data processing |