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https://sites.google.com/view/deep-net-watch-hh5w   U.S.Colonial Pipeline Hackers, DarkSide, Apologize, Say Goal 'Is to Make Money'The pipeline, which stretches more than 5,500 miles and carries 45 percent of the East Coast's supply of diesel, petrol and jet fuel, was taken offline over the weekend after a cyberattack.   https://sites.google.com/view/deep-net-watch-hh5w/deep-web-links/how-to-get-into-deep-web   Governor, it's easy. If you give me fifty million credits, no one has to die. I can stop the Emergences, and you'll save your people's lives.  https://sites.google.com/view/nexus-darknet-hub-z6k7/market-links/nexus-link   The Dark Web encompasses a spectrum of content, ranging from legitimate and privacy-focused platforms to those associated with illicit activities. Marketplaces on the Dark Web facilitate the exchange of illegal goods, including drugs, firearms, stolen data, and counterfeit currency.
 
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https://sites.google.com/view/darknet-pulse-jvym/drug-reviews/top-darknet-drug-sites   Intellectual property and sensitive financial information are prime targets.   https://sites.google.com/view/darknet-market-hub-mbbe/tordex-listings/tordex-darknet   In order to investigate the role of direct transactions between market participants, we now analyse the evolution of the S2S network, i.e., the network of the U2U transactions involving only sellers. The nodes of the S2S network are active sellers (i.e., sellers that are trading at the time) and two sellers are connected by an edge if at least one transaction was made between them during the considered snapshot period. Although the S2S network is composed only of U2U transactions, all categories of sellers (i.e, market-only, U2U-only, and market-U2U) are present in the S2S network. For instance, market-only sellers are entities classified as sellers only in markets, but that may promote U2U transactions with other sellers, hence being part of the S2S network. Therefore, the S2S network can be seen as a proxy for a distribution network of illegal products.  https://sites.google.com/view/darknet-market-hub-mbbe/market-links/silk-road-market-url   First, we propose a simple algorithm to identify buyers and sellers. Importantly, the algorithm returns reasonable estimates for the number of sellers when compared against a benchmark of nine DWMs where estimates exist. Then, we reveal a concentration of activity around an elite group of participants, where a large fraction of the trading volume is driven by a small number of players. Specifically, we uncover distinct types of buyers and sellers based on their activity between markets and the U2U network, and detect a shift in the ecosystem’s activity towards the U2U network after a major external shock in the markets. We find that trading properties of buyers and sellers reflect the dominance of DWMs in the ecosystem. By formally describing the ecosystem of DWMs as a temporal network where nodes are the entities and directed edges are transactions pointing from source to destination, we consider different networks of buyers and sellers, promoting different functions in the ecosystem. In particular, we analyse networks of ‘multihomers’, defined as users that are simultaneously trading in multiple markets. We show that these users play a crucial role in the connectivity of the ecosystem because they act as connectors between markets. Analogously, we identify and characterise ‘multisellers’ (i.e., multihomers that are sellers) and ‘multibuyers’ (i.e., multihomers that are buyers). Furthermore, we analyse the seller-to-seller (S2S) network, i.e., the network composed only of transactions among sellers, which can be regarded as a supply chain network of illicit goods and services. We highlight that these networks exhibit different resilience regimes in the presence of external shocks, the ecosystem’s resilience being mostly guaranteed by the network of buyers rather than sellers.ResultsBuyers and sellersTo characterize the structure and dynamics of the ecosystem of DWMs, we start by classifying all traders either as buyers or sellers. We implement a method of classification based on exchanged money, number of transactions, and time activity for each entity, as illustrated in Fig.
 
 
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