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1. A Comparative Study of Dark Patterns Across Web and Mobile Modalities

   https://doi.org/10.1145/3479521  

   Gunawan, Johanna; Pradeep, Amogh; Choffnes, David; Hartzog, Woodrow; Wilson, Christo (October 2021, Proceedings of the ACM on Human-Computer Interaction)

   Dark patterns are user interface elements that can influence a person's behavior against their intentions or best interests. Prior work identified these patterns in websites and mobile apps, but little is known about how the design of platforms might impact dark pattern manifestations and related human vulnerabilities. In this paper, we conduct a comparative study of mobile application, mobile browser, and web browser versions of 105 popular services to investigate variations in dark patterns across modalities. We perform manual tests, identify dark patterns in each service, and examine how they persist or differ by modality. Our findings show that while services can employ some dark patterns equally across modalities, many dark patterns vary between platforms, and that these differences saddle people with inconsistent experiences of autonomy, privacy, and control. We conclude by discussing broader implications for policymakers and practitioners, and provide suggestions for furthering dark patterns research. 

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2. A Review of Dark Web: Trends and Future Directions

   https://doi.org/10.1109/COMPSAC54236.2022.00283  

   Shahriar Sobhan, Timothy Williams (June 2022, IEEE Conference on Computers, Software & Applications)

   The dark web is often discussed in taboo by many who are unfamiliar with the subject. However, this paper takes a dive into the skeleton of what constructs the dark web by compiling the research of published essays. The Onion Router (TOR) and other discussed browsers are specialized web browsers that provide anonymity by going through multiple servers and encrypted networks between the host and client, hiding the IP address of both ends. This provides difficulty in terms of controlling or monitoring the dark web, leading to its popularity in criminal underworlds. In this work, we provide an overview of data mining and penetration testing tools that are being widely used to crawl and collect data. We compare the tools to provide strengths and weaknesses of the tools while providing challenges of harnessing massive data from dark web using crawlers and penetration testing tools including machine learning (ML) techniques. Despite the effort to crawl dark web has progressed, there are still rooms to advance existing approaches to combat the ever-changing landscape of the dark web. 

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3. Classifying Dark Web Executables Using Public Malware Tools

   Stewart, B; Vessel, B; Glisson, B (January 2025, ScholarSpace - HICSS 2025 - https://hdl.handle.net/10125/109318)

   The proliferation of malware in today’s society continues to impact industry, government, and academic organizations. The Dark Web provides cyber criminals with a venue to exchange and store malicious code and malware. Hence, this research develops a crawler to harvest source code, scripts, and executable files that are freely available on the Dark Web to investigate the proliferation of malware. Harvested executable files are analyzed with publicly accessible malware analysis tool services, including VirusTotal, Hybrid Analysis, and MetaDefender Cloud. The crawler crawls over 15 million web pages and collects over 20 thousand files consisting of code, scripts, and executable files. Analysis of the data examines the distribution of files collected from the Dark Web, the differences in the results between the analysis services, and the malicious classification of files. The results reveal that about 30% of the harvested executable files are considered malicious by the malware analysis tools. 

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4. Linking Personally Identifiable Information from the Dark Web to the Surface Web: A Deep Entity Resolution Approach

   https://doi.org/10.1109/ICDMW51313.2020.00072  

   Lin, Fangyu; Liu, Yizhi; Ebrahimi, Mohammadreza; Ahmad-Post, Zara; Hu, James Lee; Xin, Jingyu; Samtani, Sagar; Li, Weifeng; Chen, Hsinchun (November 2020, International Conference on Data Mining Workshops (ICDMW))

   null (Ed.)

   The information privacy of the Internet users has become a major societal concern. The rapid growth of online services increases the risk of unauthorized access to Personally Identifiable Information (PII) of at-risk populations, who are unaware of their PII exposure. To proactively identify online at-risk populations and increase their privacy awareness, it is crucial to conduct a holistic privacy risk assessment across the internet. Current privacy risk assessment studies are limited to a single platform within either the surface web or the dark web. A comprehensive privacy risk assessment requires matching exposed PII on heterogeneous online platforms across the surface web and the dark web. However, due to the incompleteness and inaccuracy of PII records in each platform, linking the exposed PII to users is a non-trivial task. While Entity Resolution (ER) techniques can be used to facilitate this task, they often require ad-hoc, manual rule development and feature engineering. Recently, Deep Learning (DL)-based ER has outperformed manual entity matching rules by automatically extracting prominent features from incomplete or inaccurate records. In this study, we enhance the existing privacy risk assessment with a DL-based ER method, namely Multi-Context Attention (MCA), to comprehensively evaluate individuals’ PII exposure across the different online platforms in the dark web and surface web. Evaluation against benchmark ER models indicates the efficacy of MCA. Using MCA on a random sample of data breach victims in the dark web, we are able to identify 4.3% of the victims on the surface web platforms and calculate their privacy risk scores. 

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5. Counteracting Dark Web Text-Based CAPTCHA with Generative Adversarial Learning for Proactive Cyber Threat Intelligence

   https://doi.org/10.1145/3505226  

   Zhang, Ning; Ebrahimi, Mohammadreza; Li, Weifeng; Chen, Hsinchun (June 2022, ACM Transactions on Management Information Systems)

   Automated monitoring of dark web (DW) platforms on a large scale is the first step toward developing proactive Cyber Threat Intelligence (CTI). While there are efficient methods for collecting data from the surface web, large-scale dark web data collection is often hindered by anti-crawling measures. In particular, text-based CAPTCHA serves as the most prevalent and prohibiting type of these measures in the dark web. Text-based CAPTCHA identifies and blocks automated crawlers by forcing the user to enter a combination of hard-to-recognize alphanumeric characters. In the dark web, CAPTCHA images are meticulously designed with additional background noise and variable character length to prevent automated CAPTCHA breaking. Existing automated CAPTCHA breaking methods have difficulties in overcoming these dark web challenges. As such, solving dark web text-based CAPTCHA has been relying heavily on human involvement, which is labor-intensive and time-consuming. In this study, we propose a novel framework for automated breaking of dark web CAPTCHA to facilitate dark web data collection. This framework encompasses a novel generative method to recognize dark web text-based CAPTCHA with noisy background and variable character length. To eliminate the need for human involvement, the proposed framework utilizes Generative Adversarial Network (GAN) to counteract dark web background noise and leverages an enhanced character segmentation algorithm to handle CAPTCHA images with variable character length. Our proposed framework, DW-GAN, was systematically evaluated on multiple dark web CAPTCHA testbeds. DW-GAN significantly outperformed the state-of-the-art benchmark methods on all datasets, achieving over 94.4% success rate on a carefully collected real-world dark web dataset. We further conducted a case study on an emergent Dark Net Marketplace (DNM) to demonstrate that DW-GAN eliminated human involvement by automatically solving CAPTCHA challenges with no more than three attempts. Our research enables the CTI community to develop advanced, large-scale dark web monitoring. We make DW-GAN code available to the community as an open-source tool in GitHub. 

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