Biblio

Anonymity is one of the biggest concerns in web security and traffic management. Though web users are concerned about privacy and security various methods are being adopted in making the web more vulnerable. Browsing the web anonymously not only threatens the integrity but also questions the motive of such activity. It is important to classify the network traffic and prevent source and destination from hiding with each other unless it is for benign activity. The paper proposes various methods to classify the dark web at different levels or hierarchies. Various preprocessing techniques are proposed for feature selection and dimensionality reduction. Anon17 dataset is used for training and testing the model. Three levels of classification are proposed in the paper based on the network, traffic type, and application.

This paper presents the results of a qualitative study on discussions about two major law enforcement interventions against Dark Net Market (DNM) users extracted from relevant Reddit forums. We assess the impact of Operation Hyperion and Operation Bayonet (combined with the closure of the site Hansa) by analyzing posts and comments made by users of two Reddit forums created for the discussion of Dark Net Markets. The operations are compared in terms of the size of the discussions, the consequences recorded, and the opinions shared by forum users. We find that Operation Bayonet generated a higher number of discussions on Reddit, and from the qualitative analysis of such discussions it appears that this operation also had a greater impact on the DNM ecosystem.