2020
DOI: 10.1002/ett.4088
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Ensemble Adaboost classifier for accurate and fast detection of botnet attacks in connected vehicles

Abstract: The key characteristic of smart cities (ie, connectivity and intelligence) has enabled connected vehicles to work together to accomplish complex jobs that they are unable to perform individually. Connectivity not only being an inevitable blessing but also poses growing cybersecurity challenges for connected vehicles. The overall risk of connected vehicles is wide as the cybercriminals are nowadays applying versatile approaches (botnets, phishing, zero-days, rootkits, etc) to disrupt their communication. The… Show more

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Cited by 67 publications
(27 citation statements)
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“…1) Autonomous Vehicles: The smart transportation aspect in the smart city has a unique feature of inter/intra vehicle communication consists of the vehicle to infrastructure (V2I), vehicle to pedestrian (V2P), vehicle to vehicle (V2V), vehicle to sensors (V2S), vehicle to cloud (V2C) and vehicle to home (V2H) [175]. If done in an organized way, this communication can help solve the city's transportation infrastructure problems.…”
Section: E Future Transportationmentioning
confidence: 99%
“…1) Autonomous Vehicles: The smart transportation aspect in the smart city has a unique feature of inter/intra vehicle communication consists of the vehicle to infrastructure (V2I), vehicle to pedestrian (V2P), vehicle to vehicle (V2V), vehicle to sensors (V2S), vehicle to cloud (V2C) and vehicle to home (V2H) [175]. If done in an organized way, this communication can help solve the city's transportation infrastructure problems.…”
Section: E Future Transportationmentioning
confidence: 99%
“…The AdaBoost is model where for any subsequent model the check and elimination of observations-emissions, erroneous conclusions of the previous model are carried out [27].…”
Section: The Modeling Of the Key Risk Factors Using Machine Learning Algorithmsmentioning
confidence: 99%
“…Many researchers have tried to tackle these problems by creating their emotional corpus [20,27], trying out different feature sets [46], or using multiple machine learning models, but still, there is a lot of room for improvement. Ensemble learning helps to improve the performance of the machine learning models [17,29,33]. This prompts for further exploration of different techniques that can be used to improve cross-corpus speech emotion recognition that will enable the deployment of speech emotion recognition systems in reallife applications.…”
Section: Introductionmentioning
confidence: 99%