An observational mechanism for detection of distributed denial-of-service attacks

<span>This study proposes a continuous mechanism for detecting distributed denial of service (DDoS) attacks from network traffic data. The aims to systematically organise data and prepare them DDoS attack detection using convolutional deep-learning neural networks. proposed contains ten phases covering activities, including preprocessing, feature selection, labelling, model building, evaluation, detection, pattern identification, alert creation, notification delivery, periodical sampling. evaluation results suggested that the built based on networks relevant features provided 97.2% accuracy. designed holistic considers systematic management monitoring good performance detection. could provide solution enhance existing methods In addition, it generally contributes cybersecurity body knowledge.</span>