Year: 2026
Type: article
Abstract: The accurate labeling of darknet traffic plays a vital role in real-time cybersecurity systems, as it enables the reliable identification and control of encrypted network applications. State-of-the-art studies have depended mainly on traditional machine learning with public datasets; however, incorporating deep learning (DL) techniques to analyze darknet traffic is still not effectively explored. This paper presented a unique DL-based framework. It integrated discriminative feature selection with an image-based representation of traffic. The work methodology applies the extraction of the most informative features from raw network flows and transforms them into grayscale images, enabling the effective capture of spatial patterns. Those images will be further processed by a hybrid conventional neural network (CNN) and bidirectional long short-term memory (BiLSTM) architecture that leverages the strengths of the CNN in terms of spatial feature extraction, with the modeling of bidirectional temporal dependencies of BiLSTM. For the model testing, two independent encrypted traffic datasets were combined to build a unified and diversified darknet traffic benchmark. The achieved results prove that the proposed hybrid architecture can achieve as high as 89% classification accuracy with an excellent detection and classification capability for darknet traffic. It confirmed a significant performance improvement of the encrypted traffic analysis by integrating feature selection and image-based DL.
Source: Electronics
Institutions: Middle Technical University, Brunel University of London
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
OpenAlex ID: W7130504318