Data Classification of Dark Web using SVM and S3VM

Abstract

Abstract: There are many issues regarding the dark web structural Type. It also increases the number of cybercrimes like illegal trade, forums, Terrorist activity. By understanding online criminal’s actions are challenging because the data is available in a very great extent amount. In a recent day the Online crimes are increasing all over the world. The data related to different types of frauds and scams, such as phishing schemes, identity theft etc. The data and discussion related to the act of hacking (hacktivist) activities, this often involve political or social causes. In some parts of dark web might be used for anonymous communication and the losing of sensitive information to explore wrong doing by governments or corporations. But in some countries the dark web might be used as a means to access information and content that is hardly restricted. The primary focus of this research is to develop a hybrid classification model that combines the strengths of deep learning and natural language processing algorithms. The model leverages a curated dataset of Dark Web content, meticulously labeled by content category, ranging from illegal commerce to cyber threats. By extracting relevant features from the textual and visual components of the data, the model demonstrates superior accuracy in distinguishing between different content categories