Crawling and cluster hidden web using crawler framework and fuzzy-KNN

Rahayuda, I Gede Surya; Santiari, Ni Putu Linda

doi:10.1109/citsm.2017.8089225

Cited by 13 publications

(6 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, additional terms such as hidden web or invisible web are used [24,41,44,51]. However, these terms are also used inconsistently and are indicated as synonyms for darknet, deep web or dark web.…”

Section: Discussionmentioning

confidence: 99%

Inconsistencies in Darknet Researches

Platzer

Lux

2023

JCSANDM

View full text Add to dashboard Cite

The darknet terminology is not used consistently among scientific research papers. This can lead to difficulties in regards to the applicability and the significance of the results and also facilitates misinterpretation of them. As a consequence, comparisons of the different works are complicated. In this paper, we conduct a review of previous darknet research papers in order to elaborate the distribution of the inconsistent usage of the darknet terminology. Overall, inconsistencies in darknet terminology in 63 out of 97 papers were observed. The most common statement indicated that the dark web is a part of the deep web. 19 papers equate the terms darknet and dark web. Others do not distinguish between dark web and deep web, or between deep web and darknet.

show abstract

Section: Discussionmentioning

confidence: 99%

Inconsistencies in Darknet Researches

Platzer

Lux

2023

JCSANDM

View full text Add to dashboard Cite

show abstract

“…Numerous tools have been used to analyze the extracted data, including ad hoc Java, and python scripts. The TOR DarkWeb was crawled by Rahayuda and Santiari (2017) [32], who focused on nine different domains, and described the various kinds of activities and services found on the different domains. At the end of their study, they revealed how certain domains purposely isolate themselves from the other TOR.…”

Section: Related Workmentioning

confidence: 99%

Dark Web Illegal Activities Crawling and Classifying Using Data Mining Techniques

Alaidi

Al_airaji

Alrikabi

et al. 2022

Int. J. Interact. Mob. Technol.

View full text Add to dashboard Cite

Dark web is a canopy concept that denotes any kind of illicit activities carried out by anonymous persons or organizations, thereby making it difficult to trace. The illicit content on the dark web is constantly updated and changed. The collection and classification of such illegal activities are challenging tasks, as they are difficult and time-consuming. This problem has in recent times emerged as an issue that requires quick attention from both the industry and academia. To this end, efforts have been made in this article a crawler that is capable of collecting dark web pages, cleaning them, and saving them in a document database, is proposed. The crawler carries out an automatic classification of the gathered web pages into five classes. The classifiers used in classifying the pages include Linear Support Vector Classifier (SVC), Naïve Bayes (NB), and Document Frequency (TF-IDF). The experimental results revealed that an accuracy rate of 92% and 81% were achieved by SVC and NB, respectively.

show abstract

“…The analysis of the data has been carried out with several tools, including Weka and ad hoc Java and Python scripts. Rahayuda and Santiari [11] crawled the TOR Dark Web, focusing on nine types of domains and defining the services or information hosted by the various domains. Their findings demonstrate how specific types of domains intentionally detach themselves from other TORs.…”

Section: Related Workmentioning

confidence: 99%

Classifying Illegal Activities on Tor Network using Hybrid Technique

Alshammery

Aljuboori

2022

eijs

View full text Add to dashboard Cite

With the freedom offered by the Deep Web, people have the opportunity to express themselves freely and discretely, and sadly, this is one of the reasons why people carry out illicit activities there. In this work, a novel dataset for Dark Web active domains known as crawler-DB is presented. To build the crawler-DB, the Onion Routing Network (Tor) was sampled, and then a web crawler capable of crawling into links was built. The link addresses that are gathered by the crawler are then classified automatically into five classes. The algorithm built in this study demonstrated good performance as it achieved an accuracy of 85%. A popular text representation method was used with the proposed crawler-DB crossed by two different supervised classifiers to facilitate the categorization of the Tor concealed services. The results of the experiments conducted in this study show that using the Term Frequency-Inverse Document Frequency (TF-IDF) word representation with a linear support vector classifier achieves 91% of 5 folds cross-validation accuracy when classifying a subset of illegal activities from crawler-DB, while the accuracy of Naïve Bayes was 80.6%. The good performance of the linear SVC might support potential tools to help the authorities in the detection of these activities. Moreover, outcomes are expected to be significant in both practical and theoretical aspects, and they may pave the way for further research.

show abstract

Crawling and cluster hidden web using crawler framework and fuzzy-KNN

Cited by 13 publications

References 5 publications

Inconsistencies in Darknet Researches

Inconsistencies in Darknet Researches

Dark Web Illegal Activities Crawling and Classifying Using Data Mining Techniques

Classifying Illegal Activities on Tor Network using Hybrid Technique

Contact Info

Product

Resources

About