The New Deep Web Drug Market Place (And the role of Bitcoin as a currency for drugs)

Drugs are a true menace to our society today. “Drug use on the rise” is an increasingly common headline in newspapers and it is well-known that this is the case. With the topic of drugs becoming more and more common in popular media, youngsters are especially influenced to try drugs. This is not a new problem, as such, and has been a relevant issue in modern society. Coupled with this, the internet plays a huge role in spreading information about emerging drugs (such as synthetic and ‘designer’ drugs). [2] This paper aims to understand the role of the internet’s Deep Web [3] and Bitcoin (and other Crypto currencies) in dealing drugs online.

Socio-Political Considerations of Informing Technology in the 21st Century

This chapter addresses socio-political issues surrounding informing technology in the 21st century. The chapter begins by considering the role that computers and informing technology have played in US elections. The chapter then critically examines what prosperity informing technology and turbo-capitalism has brought society. Next, global indexes that measure human well-being are considered within the broader context of growing economic equalities. The role that trade unions play in such measures is also considered. The chapter next considers the relationship between informing technology and the deep web and dark webs as well as its relation to corruption. Attention is then paid to the relation between informing technology and democracy as well as the socio-political impacts of hackers. The chapter concludes by considering the role informing technology has played in dictatorships and its role in the 2020 coronavirus pandemic as well as the positive impacts of hackers.

Threats from the Dark: A Review over Dark Web Investigation Research for Cyber Threat Intelligence

From proactive detection of cyberattacks to the identification of key actors, analyzing contents of the Dark Web plays a significant role in deterring cybercrimes and understanding criminal minds. Researching in the Dark Web proved to be an essential step in fighting cybercrime, whether with a standalone investigation of the Dark Web solely or an integrated one that includes contents from the Surface Web and the Deep Web. In this review, we probe recent studies in the field of analyzing Dark Web content for Cyber Threat Intelligence (CTI), introducing a comprehensive analysis of their techniques, methods, tools, approaches, and results, and discussing their possible limitations. In this review, we demonstrate the significance of studying the contents of different platforms on the Dark Web, leading new researchers through state-of-the-art methodologies. Furthermore, we discuss the technical challenges, ethical considerations, and future directions in the domain.

Web-Based Research in Psychology

The present article reviews web-based research in psychology. It captures principles, learnings, and trends in several types of web-based research that show similar developments related to web technology and its major shifts (e.g., appearance of search engines, browser wars, deep web, commercialization, web services, HTML5…) as well as distinct challenges. The types of web-based research discussed are web surveys and questionnaire research, web-based tests, web experiments, Mobile Experience Sampling, and non-reactive web research, including big data. A number of web-based methods are presented and discussed that turned out to become important in research methodology. These are one-item-one-screen design, seriousness check, instruction manipulation and other attention checks, multiple site entry technique, subsampling technique, warm-up technique, and web-based measurement. Pitfalls and best practices are described then, especially regarding dropout and other non-response, recruitment of participants, and interaction between technology and psychological factors. The review concludes with a discussion of important concepts that have developed over 25 years and an outlook on future developments in web-based research.

DEEP WEB – HIDDEN WEB. WHAT WE DON'T KNOW ABOUT ON THE INTERNET OR WHAT IS WELL HIDDEN FROM US

In this article, the study of the Deep Web – "Deep Web" is carried out. The article examines the way how this network appeared and spread, considers its positive and negative sides, studies such issues as: the concept, types, forms and characteristics, as well as compares it with the hidden Internet and the "black Internet" (DarkNet) and suggests methods of personal information security.

Ranked Deep Web Page Detection Using Reinforcement Learning and Query Optimization

This paper proposes a novel algorithm based on reinforcement learning-entitled asynchronous advantage actor-critic (A3C). Overflow queries are optimized to crawl the ranked deep web. A3C assigns the reward and penalty to the various queries. Queries are derived from the domain-based taxonomy that helps to fill the search forms. Overflow queries are the collection of queries that match with more than k number of results and only top k matched results are retrieved. Low ranked documents beyond k results are not accessible and lead to low coverage. Overflow queries are optimized to convert into non-overflow queries based on the proposed technique and lead to more coverage. As of yet, no research work has been explored by using A3C with taxonomy in the domain of ranked deep web. The experimental results show that the proposed technique outperforms the three other techniques (i.e., document frequency, random query, and high frequency) in terms of average improvement metric by 26%, 69%, and 92%, respectively.

Full-Page Wrapper Generation for Unsupervised Deep Web Data Extraction

<div>Web data extraction is a key component in many business intelligence tasks, such as data transformation, exchange, and analysis. Many approaches have been proposed, with either labeled training examples (supervised) or annotation-free training pages (unsupervised). However, most research focuses on extraction effectiveness. Not much attention has been paid to extraction efficiency. In fact, most unsupervised web data extraction ignores wrapper generation because they could work alone without any supervision. </div><div>In this paper, we argue that wrapper generation for unsupervised web data extraction is as important as supervised wrapper induction because the generated wrappers could work more efficiently without sophisticated analysis during testing. We consider two approaches for wrapper generation: schema-guided finite-state machine (FSM) approaches and data-driven machine learning (ML) approaches. We exploit unique mandatory templates to improve the FSM-based wrapper, and proposed two convolutional neural network (CNN)-based models for sequence-labeling. The experimental results show that the FSM wrapper performs well even with small training data, while the CNN-based models require more training pages to achieve the same effectiveness but are more efficient with GPU support. Furthermore, FSM wrappers can work as a filter to reduce the number of training pages and advance the learning curve for wrapper generation.</div>

Full-Page Wrapper Generation for Unsupervised Deep Web Data Extraction

<div>Web data extraction is a key component in many business intelligence tasks, such as data transformation, exchange, and analysis. Many approaches have been proposed, with either labeled training examples (supervised) or annotation-free training pages (unsupervised). However, most research focuses on extraction effectiveness. Not much attention has been paid to extraction efficiency. In fact, most unsupervised web data extraction ignores wrapper generation because they could work alone without any supervision. </div><div>In this paper, we argue that wrapper generation for unsupervised web data extraction is as important as supervised wrapper induction because the generated wrappers could work more efficiently without sophisticated analysis during testing. We consider two approaches for wrapper generation: schema-guided finite-state machine (FSM) approaches and data-driven machine learning (ML) approaches. We exploit unique mandatory templates to improve the FSM-based wrapper, and proposed two convolutional neural network (CNN)-based models for sequence-labeling. The experimental results show that the FSM wrapper performs well even with small training data, while the CNN-based models require more training pages to achieve the same effectiveness but are more efficient with GPU support. Furthermore, FSM wrappers can work as a filter to reduce the number of training pages and advance the learning curve for wrapper generation.</div>