Design of Intrusion Detection System based on Cyborg intelligence for security of Cloud Network Traffic of Smart Cities
Abstract The Internet of things (IoT) is an important technology that is highly beneficial in establishing smart items, connections and cities. However, there are worries regarding security and privacy vulnerabilities in IoT in which some emerge from numerous sources, including cyberattacks, unsecur...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2022-08-01
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| Series: | Journal of Cloud Computing: Advances, Systems and Applications |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13677-022-00305-6 |
| _version_ | 1798042651677687808 |
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| author | Edeh Michael Onyema Surjeet Dalal Carlos Andrés Tavera Romero Bijeta Seth Praise Young Mohd Anas Wajid |
| author_facet | Edeh Michael Onyema Surjeet Dalal Carlos Andrés Tavera Romero Bijeta Seth Praise Young Mohd Anas Wajid |
| author_sort | Edeh Michael Onyema |
| collection | DOAJ |
| description | Abstract The Internet of things (IoT) is an important technology that is highly beneficial in establishing smart items, connections and cities. However, there are worries regarding security and privacy vulnerabilities in IoT in which some emerge from numerous sources, including cyberattacks, unsecured networks, data, connections or communication. This paper provides an ensemble intrusion strategy based on Cyborg Intelligence (machine learning and biological intelligence) framework to boost security of IoT enabled networks utilized for network traffic of smart cities. To do this, multiple algorithms such Random Forest, Bayesian network (BN), C5.0, CART and Artificial Neural Network were investigated to determine their usefulness in identifying threats and attacks-botnets in IoT networks based on cyborg intelligence using the KDDcup99 dataset. The results reveal that the AdaBoost ensemble learning based on Cyborg Intelligence Intrusion Detection framework facilitates dissimilar network characteristics with the capacity to swiftly identify different botnet assaults efficiently. The suggested framework has obtained good accuracy, detection rate and a decreased false positive rate in comparison to other standard methodologies. The conclusion of this study would be a valuable complement to the efforts toward protecting IoT-powered networks and the accomplishment of safer smart cities. |
| first_indexed | 2024-04-11T22:38:26Z |
| format | Article |
| id | doaj.art-38aaaf77979e40d2bc18662381402e41 |
| institution | Directory Open Access Journal |
| issn | 2192-113X |
| language | English |
| last_indexed | 2024-04-11T22:38:26Z |
| publishDate | 2022-08-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Cloud Computing: Advances, Systems and Applications |
| spelling | doaj.art-38aaaf77979e40d2bc18662381402e412022-12-22T03:59:06ZengSpringerOpenJournal of Cloud Computing: Advances, Systems and Applications2192-113X2022-08-0111112010.1186/s13677-022-00305-6Design of Intrusion Detection System based on Cyborg intelligence for security of Cloud Network Traffic of Smart CitiesEdeh Michael Onyema0Surjeet Dalal1Carlos Andrés Tavera Romero2Bijeta Seth3Praise Young4Mohd Anas Wajid5Department of Mathematics and Computer Science, Coal City UniversityCollege of Computing Science and IT, Teerthanker Mahayeer UniversityCOMBA R&D Laboratory, Faculty of Engineering, Universidad Santiago de CaliDepartment of Computer Science and Engineering, B. M. Institute of Engineering & TechnologyDepartment of Linguistic Data Sciences, University of Eastern FinlandDepartment of Computer Science, Aligarh Muslim UniversityAbstract The Internet of things (IoT) is an important technology that is highly beneficial in establishing smart items, connections and cities. However, there are worries regarding security and privacy vulnerabilities in IoT in which some emerge from numerous sources, including cyberattacks, unsecured networks, data, connections or communication. This paper provides an ensemble intrusion strategy based on Cyborg Intelligence (machine learning and biological intelligence) framework to boost security of IoT enabled networks utilized for network traffic of smart cities. To do this, multiple algorithms such Random Forest, Bayesian network (BN), C5.0, CART and Artificial Neural Network were investigated to determine their usefulness in identifying threats and attacks-botnets in IoT networks based on cyborg intelligence using the KDDcup99 dataset. The results reveal that the AdaBoost ensemble learning based on Cyborg Intelligence Intrusion Detection framework facilitates dissimilar network characteristics with the capacity to swiftly identify different botnet assaults efficiently. The suggested framework has obtained good accuracy, detection rate and a decreased false positive rate in comparison to other standard methodologies. The conclusion of this study would be a valuable complement to the efforts toward protecting IoT-powered networks and the accomplishment of safer smart cities.https://doi.org/10.1186/s13677-022-00305-6CyborgEnsemble learningIoTNetwork Intrusion Detection System (NIDS)Cloud Computing |
| spellingShingle | Edeh Michael Onyema Surjeet Dalal Carlos Andrés Tavera Romero Bijeta Seth Praise Young Mohd Anas Wajid Design of Intrusion Detection System based on Cyborg intelligence for security of Cloud Network Traffic of Smart Cities Journal of Cloud Computing: Advances, Systems and Applications Cyborg Ensemble learning IoT Network Intrusion Detection System (NIDS) Cloud Computing |
| title | Design of Intrusion Detection System based on Cyborg intelligence for security of Cloud Network Traffic of Smart Cities |
| title_full | Design of Intrusion Detection System based on Cyborg intelligence for security of Cloud Network Traffic of Smart Cities |
| title_fullStr | Design of Intrusion Detection System based on Cyborg intelligence for security of Cloud Network Traffic of Smart Cities |
| title_full_unstemmed | Design of Intrusion Detection System based on Cyborg intelligence for security of Cloud Network Traffic of Smart Cities |
| title_short | Design of Intrusion Detection System based on Cyborg intelligence for security of Cloud Network Traffic of Smart Cities |
| title_sort | design of intrusion detection system based on cyborg intelligence for security of cloud network traffic of smart cities |
| topic | Cyborg Ensemble learning IoT Network Intrusion Detection System (NIDS) Cloud Computing |
| url | https://doi.org/10.1186/s13677-022-00305-6 |
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