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When the Dark Web executes an ultimate power grab that destroys all privacy, elite hacker Azi Bello uncovers a conspiracy that is complicated by a family member's recruitment into a digital terrorist organization.

The use of the unindexed web, commonly known as the deep web and dark web, to commit or facilitate criminal activity has drastically increased over the past decade. The dark web is a dangerous place where all kinds of criminal activities take place, Despite advances in web forensic techniques, tools, and methodologies, few studies have formally tackled dark and deep web forensics and the technical differences in terms of investigative techniques and artefact identification and extraction. This study proposes a novel and comprehensive protocol to guide and assist digital forensic professionals in investigating crimes committed on or via the deep and dark web. The protocol, named D2WFP, establishes a new sequential approach for performing investigative activities by observing the order of volatility and implementing a systemic approach covering all browsing-related hives and artefacts which ultimately resulted in improving the accuracy and effectiveness. Rigorous quantitative and qualitative research has been conducted by assessing the D2WFP following a scientifically sound and comprehensive process in different scenarios and the obtained results show an apparent increase in the number of artefacts recovered when adopting the D2WFP which outperforms any current industry or opensource browsing forensic tools. The second contribution of the D2WFP is the robust formulation of artefact correlation and cross-validation within the D2WFP which enables digital forensic professionals to better document and structure their analysis of host-based deep and dark web browsing artefacts.

\"This book is an easy-to-read and comprehensive guide to understanding how the Dark Web works and why you should be using it! Readers are led on a tour from how to download the platform for personal or public use, to how it can best be utilized for finding information. This guide busts myths and informs readers, remaining jargon-free\"-- Provided by publisher.

The success of the Silk Road has prompted the growth of many Dark Web marketplaces. This exponential growth has provided criminal enterprises with new outlets to sell illicit items. Thus, the Dark Web has generated great interest from academics and governments who have sought to unveil the identities of participants in these highly lucrative, yet illegal, marketplaces. Traditional Web scraping methodologies and investigative techniques have proven to be inept at unmasking these marketplace participants. This research provides an analytical framework for automating Dark Web scraping and analysis with free tools found on the World Wide Web. Using a case study marketplace, we successfully tested a Web crawler, developed using AppleScript, to retrieve the account information for thousands of vendors and their respective marketplace listings. This paper clearly details why AppleScript was the most viable and efficient method for scraping Dark Web marketplaces. The results from our case study validate the efficacy of our proposed analytical framework, which has relevance for academics studying this growing phenomenon and for investigators examining criminal activity on the Dark Web.

In the last two decades, illicit activities have dramatically increased on the Dark Web. Every year, Dark Web witnesses establishing new markets, in which administrators, vendors, and consumers aim to illegal acquisition and consumption. On the other hand, this rapid growth makes it quite difficult for law and security agencies to detect and investigate all those activities with manual analyses. In this paper, we introduce our approach of utilizing data mining techniques to produce useful patterns from a dark web market contents. We start from a brief description of the methodology on which the research stands, then we present the system modules that perform three basic missions: crawling and extracting the entire market data, data pre-processing, and data mining. The data mining methods include generating Association Rules from products’ titles, and from the generated rules, we infer conceptual compositions vendors use when promoting their products. Clustering is the second mining aspect, where the system clusters vendors and products. From the generated clusters, we discuss the common characteristics among clustered objects, find the Top Vendors, and analyze products promoted by the latter, in addition to the most viewed and sold items on the market. Overall, this approach helps in placing a dark website under investigation.

Background Protecting individual anonymity is a common practice in harm reduction (HR), as it can mitigate the fears that may prevent people from accessing services. Protecting anonymity usually means applying for services with a pseudonym. However, anonymity protection practices have diversified in current HR environments, for example, on the streets or in the Tor network, which relies on technology to guarantee exceptionally strong anonymity. Despite its importance, the individual’s need for anonymity when seeking help to reduce drug-related harm has been underexplored. Methods The research contexts included four street- and dark web-based HR services in Finland. The data consisted of service user interviews and naturally occurring conversations in the Tor network. We focused on service users’ accounts of their need for anonymity and applied the concept of relational anonymity, acknowledging that wider contextual relations intertwine with situational needs for anonymity. We asked: What kinds of needs for anonymity do service users express when discussing seeking help to reduce drug-related harm? How do service users account for their need for anonymity when seeking such help? To which kinds of contextual relations are these accounts attached? Results We identified connections between the accounts of the need for anonymity and various contextual relations: (1) excusing the need for anonymity by referring to societal relations: blaming Finnish society for stigmatising attitudes and exclusionary practices; (2) excusing the need for anonymity by referring to service system relations: blaming the service system for the risk of negative consequences from recording the use of illicit drugs; (3) justifying and excusing the need for anonymity by referring to personal relations: appealing to personal situation, feelings and experiences. Conclusions The need for anonymity reflects problematic societal relations, in which the stigma towards drug use is strong. The service users’ accounts were motivated by rational actions to avoid possible sanctions and the perceived abuse of power in Finnish society and services, which the service users deemed to have various negative consequences in their lives. Societies should promote cultural atmospheres and information sharing practices where anonymity is not needed, but services that protect anonymity are crucial in the current societal conditions.

Dark web traffic classification is an important research direction in cybersecurity; however, traditional classification methods have many limitations. Although deep learning architectures like CNN and LSTM, as well as multi-structural fusion frameworks, have demonstrated partial success, they remain constrained by shallow feature representation, localized decision boundaries, and poor generalization capacity. To improve the prediction accuracy and classification precision of dark web traffic, we propose a novel dark web traffic classification model integrating multi-channel image deep learning and a three-dimensional convolutional neural network (3D-CNN). The proposed framework leverages spatial–temporal feature fusion to enhance discriminative capability, while the 3D-CNN structure effectively captures complex traffic patterns across multiple dimensions. The experimental results show that compared to common 2D-CNN and 1D-CNN classification models, the dark web traffic classification method based on multi-channel image visual features and 3D-CNN can improve classification by 5.1% and 3.3% while maintaining a smaller total number of parameters and feature recognition parameters, effectively reducing the computational complexity of the model. In comparative experiments, 3D-CNN validates the model’s superiority in accuracy and computational efficiency compared to state-of-the-art methods, offering a promising solution for dark web traffic monitoring and security applications.

There is limited research on current traffic classification methods for dark web traffic and the classification results are not very satisfactory. To improve the prediction accuracy and classification precision of dark web traffic, a classification method (CLA) based on spatial–temporal feature fusion and an attention mechanism is proposed. When processing raw bytes, the combination of a CNN and LSTM is used to extract local spatial–temporal features from raw data packets, while an attention module is introduced to process key spatial–temporal data. The experimental results show that this model can effectively extract and utilize the spatial–temporal features of traffic data and use the attention mechanism to measure the importance of different features, thereby achieving accurate predictions of different dark web traffic. In comparative experiments, the accuracy, recall rate, and F1 score of this model are higher than those of other traditional methods.

Online marketplaces are the main engines of legal and illegal e-commerce, yet their empirical properties are poorly understood due to the absence of large-scale data. We analyze two comprehensive datasets containing 245M transactions (16B USD) that took place on online marketplaces between 2010 and 2021, covering 28 dark web marketplaces, i.e. unregulated markets whose main currency is Bitcoin, and 144 product markets of one popular regulated e-commerce platform. We show that transactions in online marketplaces exhibit strikingly similar patterns despite significant differences in language, lifetimes, products, regulation, and technology. Specifically, we find remarkable regularities in the distributions of transaction amounts, number of transactions, interevent times, and time between first and last transactions. We show that buyer behavior is affected by the memory of past interactions and use this insight to propose a model of network formation reproducing our main empirical observations. Our findings have implications for understanding market power on online marketplaces as well as intermarketplace competition, and provide empirical foundation for theoretical economic models of online marketplaces.