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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.<\/jats:p>","DOI":"10.1186\/s13677-022-00305-6","type":"journal-article","created":{"date-parts":[[2022,8,13]],"date-time":"2022-08-13T06:02:45Z","timestamp":1660370565000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":68,"title":["Design of Intrusion Detection System based on Cyborg intelligence for security of Cloud Network Traffic of Smart Cities"],"prefix":"10.1186","volume":"11","author":[{"given":"Edeh Michael","family":"Onyema","sequence":"first","affiliation":[]},{"given":"Surjeet","family":"Dalal","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4606-7222","authenticated-orcid":false,"given":"Carlos Andr\u00e9s Tavera","family":"Romero","sequence":"additional","affiliation":[]},{"given":"Bijeta","family":"Seth","sequence":"additional","affiliation":[]},{"given":"Praise","family":"Young","sequence":"additional","affiliation":[]},{"given":"Mohd Anas","family":"Wajid","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,8,13]]},"reference":[{"issue":"1","key":"305_CR1","first-page":"34","volume":"5","author":"EM Onyema","year":"2021","unstructured":"Onyema EM, Edeh CD, Gregory US, Edmond VU, Charles AC, Richard-Nnabu NE (2021) Cybersecurity awareness among undergraduate students in Enugu Nigeria. 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