The Dark Web and anonymizing technologies: legal pitfalls, ethical prospects, and policy directions from radical criminology

Davis, Shelby; Arrigo, Bruce A.

doi:10.1007/s10611-021-09972-z

Cited by 18 publications

(10 citation statements)

References 18 publications

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“…Tor and the Dark Web are widely studied (Davis & Arrigo, 2021; Dordal, 2018; Jardine, 2015; Zulkarnine et al, 2016), but usually in a similar vein: focused on the clearly illegal/illicit activities that hidden services tend to cater to. Supporters of the Dark Web frequently tout the possible benefits of anonymity software, without producing data to bolster their claims.…”

Section: Discussionmentioning

confidence: 99%

Mapping Real-World Use of the Onion Router

Ghazi‐Tehrani

2023

Journal of Contemporary Criminal Justice

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Since its inception, The Onion Router (TOR) has been discussed as an anonymizing tool used for nefarious purposes. Past scholarship has focused on publicly available lists of onion URLs containing illicit or illegal content. The current study is an attempt to move past these surface-level explanations and into a discussion of actual use data; a multi-tiered system to identify real-world TOR traffic was developed for the task. The researcher configured and deployed a fully functioning TOR “exit” node for public use. A Wireshark instance was placed between the node and the “naked” internet to collect usage data (destination URLs, length of visit, etc.), but not to deanonymize or otherwise unmask TOR users. For 6 months, the node ran and collected data 24 hr per day, which produced a data set of over 4.5 terabytes. Using Python, the researcher developed a custom tool to filter the URLs into human-readable form and to produce descriptive data. All URLs were coded and categorized into a variety of classifications, including e-commerce, banking, social networking, pornography, and cryptocurrency. Findings reveal that most TOR usage is rather benign, with users spending much more time on social networking and e-commerce sites than on those with illegal drug or pornographic content. Likewise, visits to legal sites vastly outnumber visits to illegal ones. Although most URLs collected were for English-language websites, there were a sizable amount for Russian and Chinese sites, which may demonstrate the utilization of TOR in countries where internet access is censored or monitored by government actors. Akin to other new technologies which have earned bad reputations, such as file-sharing program BitTorrent and intellectual property theft or cryptocurrency Bitcoin and online drug sales, this study demonstrates that TOR is utilized by offenders and non-offenders alike.

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Section: Discussionmentioning

confidence: 99%

Mapping Real-World Use of the Onion Router

Ghazi‐Tehrani

2023

Journal of Contemporary Criminal Justice

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“…The proliferation of the social network on the web has given rise to a number of possibilities to share and spread any type of content. The benefit of anonymity of dark web and freedom also gives the extremist groups the advantage of doing radicalized activities on the internet through social media engagement [5]. This helps these extremists and terrorist organizations to lure and influence common people into joining their organizations and promote radically motivated acts.…”

Section: Applicationsmentioning

confidence: 99%

A Review on Automated Counter Measures to Filter Radicalized Content from Social Media Applications

Tatawat¹

2022

IJSREM

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With advancements in technology and cyber warfare, terrorist and radical content is being used as a tools to spread violence and social unrest. Due to the complexity and the largeness of the data size, it is humanly infeasible to analyse the data manually or use statistical techniques. Thus machine learning based approaches are indispensable for the detection and classification of the radical and possible terrorist activity based content. In this approach, the use of social media messages in the form of tweets and messages have been considered. This paper presents a comprehensive review on filtering out potentially radical social media content based on machine learning approaches. Keywords: Social Media, Radicalized content, machine learning, accuracy

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“…Credibility and safety of information is a particular concern in the Dark Web (i.e., the darknet or anonymous web), which is a hidden part of the Internet characterized by high anonymity and unregulated content, most often accessed via The Onion Router -network (i.e., TORnetwork) [18]. Due to high anonymity and its unregulated nature, the Dark Web attracts individuals for illegal and criminal purposes, such as the trading of illegal drugs and weapons, forged documents, illegal gambling, and other criminal behaviors [19][20][21].…”

Section: Introductionmentioning

confidence: 99%