Characterizing the Redundancy of DarkWeb .onion Services

Burda, Pavlo; Boot, Coen; Allodi, Luca

doi:10.1145/3339252.3339273

Cited by 11 publications

(5 citation statements)

References 13 publications

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“…Calculated weights represent partial linearization from downstream stream feature maps of selected convolution layer and capture the importance of feature map k towards target prediction onion class label c [43]. ReLU activation function applied to obtain positively influence features towards the prediction to calculated Grad-CAM over calculated weights as in (3). The final output used to visualize and identify the regions with more importance which we have leveraged in our study to provide explanations.…”

Section: A Pathway 1 -Learning the Image Modalitymentioning

confidence: 99%

“…Recent developments in Artificial Intelligence (AI) have been effective in detecting most cyber threats with minimal human intervention which will improve cybersecurity while reducing the human errors [1]. However, cybersecurity threats originating and persisting in the dark web remain a persistent challenge due to the technical complexity while the interpretability of AI models used in dark web threat analysis applications hasn't been explored well in the existing research literature [2] [3]. The two main constituents of the Internet are the surface web and the deep web [4].…”

Section: Introductionmentioning

confidence: 99%

“…Buldin et al[33] implemented k-nearest neighbors algorithm (kNN) based text categorization method to classify Russian language onion-sites. Burda et al[3] developed a tool named MASSDEAL to automatically explore and categorize the onion services based on their textual content.…”

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confidence: 99%

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Multimodal Classification of Onion Services for Proactive Cyber Threat Intelligence Using Explainable Deep Learning

et al. 2022

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The dark web has been confronted with a significant increase in the number and variety of onion services of illegitimate and criminal intent. Anonymity, encryption, and the technical complexity of the Tor network are key challenges in detecting, disabling, and regulating such services. Instead of tracking an operational location, cyber threat intelligence can become more proactive by utilizing recent advances in Artificial Intelligence (AI) to detect and classify onion services based on the content, as well as provide an interpretation of the classification outcome. In this paper, we propose a novel multimodal classification approach based on explainable deep learning that classifies onion services based on the image and text content of each site. A Convolutional Neural Network with Gradient-weighted Class Activation Mapping (Grad-CAM) and a pre-trained word embedding with Bahdanau additive attention are the core capabilities of this approach that classify and contextualize the representative features of an onion service. We demonstrate the superior classification accuracy of this approach as well as the role of explainability in decision-making that collectively enables proactive cyber threat intelligence in the dark web.

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Section: A Pathway 1 -Learning the Image Modalitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multimodal Classification of Onion Services for Proactive Cyber Threat Intelligence Using Explainable Deep Learning

et al. 2022

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“…These allow accessing web content using alternative names derived from the encryption keys of the network nodes. (Burda et al, 2019) Research has appeared in recent years showing that the anonymity of Tor users may not be fully guaranteed due to the possibility of reconstructing of used private encryption keys (Kadianakis et al, 2018). However, the analysis (Sanchez-Rola et al, 2017) shows that a significant number of websites hidden in the Tor network use external resources, especially scripts, that can be used for the user tracking.…”

Section: User Tracking Preventionmentioning

confidence: 99%

Towards Re-Decentralized Future of the Web: Privacy, Security and Technology Development

Vojíř¹,

Kucera²

2021

AIP

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The World Wide Web (the Web) has become part of people's daily lives. Although the Web, like the Internet itself, was designed as a decentralized network, hand in hand with the increase in its interactivity Web users gradually concentrated on a limited number of platforms. As a result, providers of these large international platforms have become centres of power that can easily influence users' behaviour and what information they can access. This paper is based on an integrative literature review and its aim is to describe the development of the Web from its beginnings to the present. This development is viewed from the perspective of centralization of the Web and the reactions that this centralization has provoked, especially the current trends towards the so-called re-decentralized Web. More specifically, the paper focuses on the privacy imperative that might act as a driving force for the re-decentralized Web, and on the technological innovations enabling development of truly decentralized platforms and applications. This paper contributes to the discussion of implications that a wider adoption of the re-decentralized Web could bring in the near future.

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“…ey provided a critical discussion on possible data collection techniques for dark Web and conducted analyses on the relationship between Tor English content and its topology. Focusing on deployment and mirroring of Tor hidden services, Burda et al provided an extensive investigation on redundancy of Tor services across time and space [10]. Similarly, Gri th et al investigated the graph theoretic properties of Tor network and compared it with previous analyses conducted on the surface Web [15].…”

Section: Related Workmentioning

confidence: 99%

A First Look at References from the Dark to Surface Web World

Zabihimayvan,

Doran

2019

Preprint

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Tor is one of the most well-known networks that protects the identity of both content providers and their clients against any tracking or tracing on the Internet. So far, most research a ention has been focused on investigating the security and privacy concerns of Tor and characterizing the topic or hyperlink structure of its hidden services. However, there is still lack of knowledge about the information leakage a ributed to the linking from Tor hidden services to the surface Web. is work addresses this gap by presenting a broad evaluation of the network of referencing from Tor to surface Web and investigates to what extent Tor hidden services are vulnerable against this type of information leakage. e analyses also consider how linking to surface websites can change the overall hyperlink structure of Tor hidden services. ey also provide reports regarding the type of information and services provided by Tor domains. Results recover the dark-to-surface network as a single massive connected component where over 90% of Tor hidden services have at least one link to the surface world despite their interest in being isolated from surface Web tracking. We identify that Tor directories have closest proximity to all other Web resources and signi cantly contribute to both communication and information dissemination through the network which emphasizes on the main application of Tor as information provider to the public. Our study is the product of crawling near 2 million pages from 23,145 onion seed addresses, over a three-month period.

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Characterizing the Redundancy of DarkWeb .onion Services

Cited by 11 publications

References 13 publications

Multimodal Classification of Onion Services for Proactive Cyber Threat Intelligence Using Explainable Deep Learning

Multimodal Classification of Onion Services for Proactive Cyber Threat Intelligence Using Explainable Deep Learning

Towards Re-Decentralized Future of the Web: Privacy, Security and Technology Development

A First Look at References from the Dark to Surface Web World

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