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Title	Bot-Iot: Detection Of Attacks In Iot-Cybersecurity For Smart Transportation
ID_Doc	12793
Authors	Sharma A.; Babbar H.
Year	2023
Published	Proceedings of the 8th International Conference on Communication and Electronics Systems, ICCES 2023
DOI	http://dx.doi.org/10.1109/ICCES57224.2023.10192814
Abstract	Due to the possibility of cyberattacks, cybersecurity has emerged as one of the most crucial issues relating to IoT technology. IoT cybersecurity aims to lessen cybersecurity hazards for businesses and customers by safeguarding IoT resources and privacy protection. New cybersecurity technologies and strategies may make it possible to handle IoT security better. Internet-connected items such as smartphones, smart schooling, smart transportation and smart cities were all made possible by modern technology. The most significant application area for ML-based strategies to handle frequent attack challenges and create thought-provoking conversations is thought to be smart transportation. An efficient BoT-IoT dataset that already exists is employed for this purpose, together with a variety of attack categories and subcategories, for training and evaluating the system's dependability. Using the BoT-IoT dataset, the paper's primary objective is to deploy various machine learning techniques, including Random Forest (RF), Naive Bayes (NB), and Decision Tree (DT), to analyse the effectiveness of attacks. Using the most well-known BoT-IoT dataset, the best accuracy obtained by the machine learning methods, RF and DT is 91% and 91%, respectively. © 2023 IEEE.
Author Keywords	BoT-IoT dataset; Cyberattacks; Internet of Things; Machine Learning; Security

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