Assessing the Health of the Dark Web:

Onyango, Samuel; Steenvoorden, Emilie; Scholten, Joram; Jansen, Slinger

doi:10.1007/978-3-030-88583-0_12

Cited by 2 publications

(2 citation statements)

References 9 publications

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“…The logical structure of the Dark Web is based on protecting user identity and hiding network activities [18]. The Dark Web is often portrayed as a hub for illicit and enigmatic activities [10,19].…”

Section: Surface Web Deep Web and Dark Webmentioning

confidence: 99%

“…The Tor network is estimated to have between 2 and 2.5 million daily users [39][40][41]. Tor is designed to protect the privacy of all users, even those who may wish to conceal their identities for illicit purposes [19,42,43]. The Use of Dark Web platforms by cybercriminals has significantly increased the number of illegal products available, from a few thousand in 2013 to hundreds of thousands today [40,41].…”

Section: Surface Web Deep Web and Dark Webmentioning

confidence: 99%

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Technology Acceptance Level of Dark Web Users

Çelik,

Ziyanak

2024

Preprint

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“Dark Web” usually describes a new world of e-living space perceived as a place, free from con-straints, in which its main actors generally are criminal, unstable, and mentally disturbed individ-uals. Nowadays, advancements in information and internet technologies have made the Dark Web more accessible in recent decades. The Dark Web is a virtual world component; therefore, as-sessing users’ behavior may be crucial in understanding motivation to use it. This study aims to discuss the behavior patterns of Dark Web users within the theoretical framework of the technol-ogy acceptance model via a structural equation model. The resulting structural equation model showed that considered causal relationships were statistically significant.

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Section: Surface Web Deep Web and Dark Webmentioning

confidence: 99%

Section: Surface Web Deep Web and Dark Webmentioning

confidence: 99%

Technology Acceptance Level of Dark Web Users

Çelik,

Ziyanak

2024

Preprint

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Few Images, Many Insights : Illicit Content Detection Using a Limited Number of Images

Cascavilla,

Catolino,

Conti

et al. 2024

ACM Trans. Intell. Syst. Technol.

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The anonymity and untraceability benefits of the dark web increased its popularity exponentially. The cost of these technical benefits is that such anonymity has created a suitable womb for illicit activity. Hence—in collaboration with cybersecurity practitioners and law-enforcement agencies—the research community provided approaches for recognizing and classifying illicit activities. Most of these approaches exploit textual content from dark web markets, whereas few used images that originated from them. This paper investigates alternative techniques for recognizing illegal activities from images. The significant contributions of our work are threefold: (a) we investigate label-agnostic learning techniques like One-Shot and Few-Shot learning that use Siamese Neural Networks (SNNs). Our approach manages to handle small-scale datasets with promising accuracy. In particular, the Siamese neural network approach reaches 90.9% on 5-Shot experiments over a 10-class dataset. (b) this study's satisfactory findings facilitate the creation of potent tools to assist authorities in identifying illicit content on the web. Moreover, our proof-of-concept approach demonstrated the ability to recognize illegal images using a limited number of files, reducing the time constraint in collecting illegal images. (c) we provide a complete labelled dataset of 3570 images from 55 different categories from dark web markets that can be used for future research activities.

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Assessing the Health of the Dark Web:

Cited by 2 publications

References 9 publications

Technology Acceptance Level of Dark Web Users

Technology Acceptance Level of Dark Web Users

Few Images, Many Insights : Illicit Content Detection Using a Limited Number of Images

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