Network Attack Detection Iot by Gul Muhammad (3 results)

Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -We like to have simple and automated solutions but these simple and automated solutions in technology could also contains risks if not deal properly. IoT security and privacy concerns are needs to be focus. There can be multiple types of attack on IoT networks which can damage the device or steal the sensitive information. Therefore, artificial intelligence (AI) techniques has an ability to detect and classify an unknown network behavior by learning the network attacks patterns based on large volumes of historical data. we used Aposemat IoT-23 dataset, investigate the background and implement the machine learning algorithms such as Decision Tree, Random Forest and Naive Bayes. We also compared the accuracy among these machine learning algorithms on the IoT-23 dataset and showed the most efficient machine learning algorithm as per results by using Aposemat IoT-23 dataset, as well as showed feature engineering techniques to preprocess the mentioned dataset for detection and classification of IoT network attacks. 52 pp. Englisch.…

Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. We like to have simple and automated solutions but these simple and automated solutions in technology could also contains risks if not deal properly. IoT security and privacy concerns are needs to be focus. There can be multiple types of attack on IoT netwo.…

Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -We like to have simple and automated solutions but these simple and automated solutions in technology could also contains risks if not deal properly. IoT security and privacy concerns are needs to be focus. There can be multiple types of attack on IoT networks which can damage the device or steal the sensitive information. Therefore, artificial intelligence (AI) techniques has an ability to detect and classify an unknown network behavior by learning the network attacks patterns based on large volumes of historical data. we used Aposemat IoT-23 dataset, investigate the background and implement the machine learning algorithms such as Decision Tree, Random Forest and Naive Bayes. We also compared the accuracy among these machine learning algorithms on the IoT-23 dataset and showed the most efficient machine learning algorithm as per results by using Aposemat IoT-23 dataset, as well as showed feature engineering techniques to preprocess the mentioned dataset for detection and classification of IoT network attacks.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 52 pp. Englisch. …