An Empirical Review of Dark Web Data Classification Methods Using NLP, SVM, CNN, and GAN

The requirement for effective data classification on the Dark Web has increased following the rising sophistication and spread of illicit operations over this secretive internet segment. This paper systematically reviews and compares the state-of-the-art Dark Web data classification methods that fall into text-based, image-based, and hybrid approaches. In the review process, this research outlines their strengths, weaknesses, and common challenges while also identifying gaps. The text-based approaches made extensive use of NLP, machine learning algorithms (such as SVM), and deep learning models, most notably, RNN and CNN. These techniques are good at information processing and understanding textual content, but high variability and obfuscation tactics make the communications hard to understand most of the time. Image-based classification leverages state-of-the-art computer vision techniques, for instance, CNN and GAN. These models are effective at detecting and classifying illicit images, such as goods or services that are illegal. However, it faces problems with the low quality or morphed images that are common on the Dark Web. Hybrid approaches incorporate both text and image in the analysis so that the information provided on the Dark Web is considered in an integrated form. These models use multimodal deep learning, which involves the use of CNN for image data and RNN for textual data samples. Hybrid methods combine text and image analysis to provide a more comprehensive understanding of Dark Web data samples. The approaches integrate multimodal deep learning models where CNNs are used for image data and RNNs for text data samples. Hybrid models show promise in improving classification accuracy by capturing the contextual information from both text and image samples. The present work outlines the necessity for developing highly adaptive and robust classification methods for Dark Web data samples. By assessing the strengths and limitations of existing methods, this work provides constructive insights for researchers and practitioners striving to devise more effective methods of monitoring and intervening on the Dark Web. It further identifies crucial gaps in the current research such as, for instance, the need for more resilient models capable of handling the dynamic and obfuscated nature of the content on the Dark Web.