An intelligent method for detection and classification of darknet traffic using sequential model along with Adam and stochastic gradient decent optimisers

The clandestine nature of darknet activities poses a significant challenge to traditional cybersecurity measures, necessitating advanced techniques for effective detection and classification. Darknet traffic classification is very much needed now days as day by day the market of illegal and hidden services are being increased in the darknet. There are various machine learning-based approach has been proposed for the categorisation of darknet traffic but very few work has been done using the concept of deep learning. This research introduces an intelligent approach which leverages a sequential deep learning model to enhance the accuracy and efficiency of darknet traffic detection and classification. In the training phase, the model is exposed to a diverse dataset encompassing a wide range of darknet traffic patterns, ensuring its ability to generalise and recognise novel patterns in real-world scenarios. The proposed model has used sequential model along with the stochastic gradient descent (SGD) and Adam optimiser which successfully detect and classify the darknet traffic with the overall accuracy of 96.77%.