Improved Mitigation of Cyber Threats in IIoT for Smart Cities: A New-Era Approach and Scheme
Cybersecurity in Industrial Internet of Things (IIoT) has become critical as smart cities are becoming increasingly linked to industrial control systems (ICSs) used in critical infrastructure. Consequently, data-driven security systems for analyzing massive amounts of data generated by smart cities...
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MDPI AG
2021-03-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/6/1976 |
| _version_ | 1797541773934854144 |
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| author | Semi Park Kyungho Lee |
| author_facet | Semi Park Kyungho Lee |
| author_sort | Semi Park |
| collection | DOAJ |
| description | Cybersecurity in Industrial Internet of Things (IIoT) has become critical as smart cities are becoming increasingly linked to industrial control systems (ICSs) used in critical infrastructure. Consequently, data-driven security systems for analyzing massive amounts of data generated by smart cities have become essential. A representative method for analyzing large-scale data is the game bot detection approach used in massively multiplayer online role-playing games. We reviewed the literature on bot detection methods to extend the anomaly detection approaches used in bot detection schemes to IIoT fields. Finally, we proposed a process wherein the data envelopment analysis (DEA) model was applied to identify features for efficiently detecting anomalous behavior in smart cities. Experimental results using random forest show that our extracted features based on a game bot can achieve an average F1-score of 0.99903 using 10-fold validation. We confirmed the applicability of the analyzed game-industry methodology to other fields and trained a random forest on the high-efficiency features identified by applying a DEA, obtaining an F1-score of 0.997 using the validation set approach. In this study, an anomaly detection method for analyzing massive smart city data based on a game industry methodology was presented and applied to the ICS dataset. |
| first_indexed | 2024-03-10T13:20:28Z |
| format | Article |
| id | doaj.art-9d8804be0b744b77a96b9a905056cd57 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T13:20:28Z |
| publishDate | 2021-03-01 |
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| series | Sensors |
| spelling | doaj.art-9d8804be0b744b77a96b9a905056cd572023-11-21T10:03:53ZengMDPI AGSensors1424-82202021-03-01216197610.3390/s21061976Improved Mitigation of Cyber Threats in IIoT for Smart Cities: A New-Era Approach and SchemeSemi Park0Kyungho Lee1School of Cybersecurity, Korea University, Seoul 02841, KoreaSchool of Cybersecurity, Korea University, Seoul 02841, KoreaCybersecurity in Industrial Internet of Things (IIoT) has become critical as smart cities are becoming increasingly linked to industrial control systems (ICSs) used in critical infrastructure. Consequently, data-driven security systems for analyzing massive amounts of data generated by smart cities have become essential. A representative method for analyzing large-scale data is the game bot detection approach used in massively multiplayer online role-playing games. We reviewed the literature on bot detection methods to extend the anomaly detection approaches used in bot detection schemes to IIoT fields. Finally, we proposed a process wherein the data envelopment analysis (DEA) model was applied to identify features for efficiently detecting anomalous behavior in smart cities. Experimental results using random forest show that our extracted features based on a game bot can achieve an average F1-score of 0.99903 using 10-fold validation. We confirmed the applicability of the analyzed game-industry methodology to other fields and trained a random forest on the high-efficiency features identified by applying a DEA, obtaining an F1-score of 0.997 using the validation set approach. In this study, an anomaly detection method for analyzing massive smart city data based on a game industry methodology was presented and applied to the ICS dataset.https://www.mdpi.com/1424-8220/21/6/1976anomaly detectiondata envelopment analysissmart cityindustrial control systemscybersecurity |
| spellingShingle | Semi Park Kyungho Lee Improved Mitigation of Cyber Threats in IIoT for Smart Cities: A New-Era Approach and Scheme Sensors anomaly detection data envelopment analysis smart city industrial control systems cybersecurity |
| title | Improved Mitigation of Cyber Threats in IIoT for Smart Cities: A New-Era Approach and Scheme |
| title_full | Improved Mitigation of Cyber Threats in IIoT for Smart Cities: A New-Era Approach and Scheme |
| title_fullStr | Improved Mitigation of Cyber Threats in IIoT for Smart Cities: A New-Era Approach and Scheme |
| title_full_unstemmed | Improved Mitigation of Cyber Threats in IIoT for Smart Cities: A New-Era Approach and Scheme |
| title_short | Improved Mitigation of Cyber Threats in IIoT for Smart Cities: A New-Era Approach and Scheme |
| title_sort | improved mitigation of cyber threats in iiot for smart cities a new era approach and scheme |
| topic | anomaly detection data envelopment analysis smart city industrial control systems cybersecurity |
| url | https://www.mdpi.com/1424-8220/21/6/1976 |
| work_keys_str_mv | AT semipark improvedmitigationofcyberthreatsiniiotforsmartcitiesaneweraapproachandscheme AT kyungholee improvedmitigationofcyberthreatsiniiotforsmartcitiesaneweraapproachandscheme |