The Digital Detective's Discourse - A toolset for forensically sound collaborative dark web content annotation and collection

Bergman, Jesper; Popov, Oliver

doi:10.15394/jdfsl.2022.1740

Cited by 5 publications

(3 citation statements)

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“…The documents to be selected for review are referred to as "studies" by Kitchenham [47] In this work, the research database that was utilised for retrieving studies was Scopus 7 .…”

Section: B Search Strategymentioning

confidence: 99%

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Exploring Dark Web Crawlers: A Systematic Literature Review of Dark Web Crawlers and Their Implementation

Bergman

Popov

2023

IEEE Access

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Strong encryption algorithms and reliable anonymity routing have made cybercrime investigation more challenging. Hence, one option for law enforcement agencies (LEAs) is to search through unencrypted content on the Internet or anonymous communication networks (ACNs). The capability of automatically harvesting web content from web servers enables LEAs to collect and preserve data prone to serve as potential leads, clues, or evidence in an investigation. Although scientific studies have explored the field of web crawling soon after the inception of the web, few research studies have thoroughly scrutinised web crawling on the "dark web" or via ACNs such as I2P, IPFS, Freenet, and Tor. The current paper presents a systematic literature review (SLR) that examines the prevalence and characteristics of dark web crawlers. From a selection of 58 peer-reviewed articles mentioning crawling and the dark web, 34 remained after excluding irrelevant articles. The literature review showed that most dark web crawlers were programmed in Python, using either Selenium or Scrapy as the web scraping library. The knowledge gathered from the systematic literature review was used to develop a Tor-based web crawling model into an already existing software toolset customised for ACN-based investigations. Finally, the performance of the model was examined through a set of experiments. The results indicate that the developed crawler was successful in scraping web content from both clear and dark web pages, and scraping dark marketplaces on the Tor network. The scientific contribution of this paper entails novel knowledge concerning ACN-based web crawlers. Furthermore, it presents a model for crawling and scraping clear and dark websites for the purpose of digital investigations. The conclusions include practical implications of dark web content retrieval and archival, such as investigation clues and evidence, and the related future research topics.INDEX TERMS cybercrime, digital forensics, systematic literature review, dark web crawling, Tor

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“…The documents to be selected for review are referred to as "studies" by Kitchenham [47] In this work, the research database that was utilised for retrieving studies was Scopus 7 .…”

Section: B Search Strategymentioning

confidence: 99%

“…In a previous research article, a toolset called D3, developed for annotating, highlighting, collecting, and analysing .onion sites was presented [7]. However, the web crawler in that toolset was a primitive one that only served as a proof-ofconcept component.…”

Section: Implementation Of a Tor Crawlermentioning

confidence: 99%

Exploring Dark Web Crawlers: A Systematic Literature Review of Dark Web Crawlers and Their Implementation

Bergman

Popov

2023

IEEE Access

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“…Collaborative monitoring and sharing of information across cybersecurity agencies are currently the most effective strategy to prevent or minimize cybersecurity threats [9]. Threat sharing platforms are critical in sharing information across multiple interested parties [10] [11].…”

Section: Introductionmentioning

confidence: 99%

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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Forensic Communication Analysis: Challenges and Opportunities

Xi,

Jaeckel,

Spranger

et al. 2024

2024 IEEE International Conference on Cyber Security and Resilience (CSR)

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The Digital Detective's Discourse - A toolset for forensically sound collaborative dark web content annotation and collection

Cited by 5 publications

References 0 publications

Exploring Dark Web Crawlers: A Systematic Literature Review of Dark Web Crawlers and Their Implementation

Exploring Dark Web Crawlers: A Systematic Literature Review of Dark Web Crawlers and Their Implementation

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

Forensic Communication Analysis: Challenges and Opportunities

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