<![CDATA[Internet of Things (IoT) is an architecture that connects large numbers of smart devices in today's modern global network system. Distributed denial of services (DDoS) attacks are one of the most common types of cyber attacks, targeting servers or networks with the aim of disrupting their normal activities. Although real-time detection and mitigation of DDoS attacks is difficult to achieve, the solution would be invaluable as attacks can cause significant damage. This research utilizes artificial intelligence (AI) to classify attacks on Internet of Things (IoT) network traffic. The resulting classification of DDOS attacks from all types of attacks, namely SYN, ACK, UDP, and UDPplain. The application of a deep learning model with the Convolutional Neural Network (CNN) algorithm is used to classify normal traffic from DDoS cyber attacks. The CNN algorithm performs very well in the classification process with an accuracy of 99%. Next, we plan to build a new model to block or mitigate DDoS attacks based on the output of the CNN classification algorithm used in this research.]]>
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